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    Genome-wide insights into population structure and host specificity of Campylobacter jejuni

    1.Burnham, P. M. & Hendrixson, D. R. Campylobacter jejuni: Collective components promoting a successful enteric lifestyle. Nat. Rev. Microbiol. 16, 551–565. https://doi.org/10.1038/s41579-018-0037-9 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    2.Humphrey, T., O’Brien, S. & Madsen, M. Campylobacters as zoonotic pathogens: A food production perspective. Int. J. Food Microbiol. 117, 237–257. https://doi.org/10.1016/j.ijfoodmicro.2007.01.006 (2007).Article 
    PubMed 

    Google Scholar 
    3.Hale, C. R. et al. Estimates of enteric illness attributable to contact with animals and their environments in the United States. Clin. Infect. Dis. 54, S472–S479. https://doi.org/10.1093/cid/cis051 (2012).Article 
    PubMed 

    Google Scholar 
    4.Friedman, C. R. et al. Risk factors for sporadic Campylobacter infection in the United States: A case-control study in FoodNet sites. Clin. Infect. Dis. 38(Suppl 3), S285–S296. https://doi.org/10.1086/381598 (2004).Article 
    PubMed 

    Google Scholar 
    5.Marder, E. P. et al. Incidence and trends of infections with pathogens transmitted commonly through food and the effect of increasing use of culture-independent diagnostic tests on surveillance—Foodborne diseases active surveillance network, 10 U.S. Sites, 2013–2016. MMWR. Morb. Mortal. Wkly. Rep. 66, 397–403. https://doi.org/10.15585/mmwr.mm6615a1 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Kaakoush, N. O., Castaño-Rodríguez, N., Mitchell, H. M. & Man, S. M. Global epidemiology of Campylobacter infection. Clin. Microbiol. Rev. 28, 687–720. https://doi.org/10.1128/CMR.00006-15 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.Didelot, X. & Falush, D. Inference of bacterial microevolution using multilocus sequence data. Genetics 175, 1251–1266. https://doi.org/10.1534/genetics.106.063305 (2007).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Sheppard, S. K. et al. Niche segregation and genetic structure of Campylobacter jejuni populations from wild and agricultural host species. Mol. Ecol. 20, 3484–3490. https://doi.org/10.1111/j.1365-294X.2011.05179.x (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Griekspoor, P. et al. Marked host specificity and lack of phylogeographic population structure of Campylobacter jejuni in wild birds. Mol. Ecol. 22, 1463–1472. https://doi.org/10.1111/mec.12144 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Ogden, I. D. et al. Campylobacter excreted into the environment by animal sources: Prevalence, concentration shed, and host association. Foodborne Pathog. Dis. 6, 1161–1170. https://doi.org/10.1089/fpd.2009.0327 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Dearlove, B. L. et al. Rapid host switching in generalist Campylobacter strains erodes the signal for tracing human infections. ISME J. 10, 721–729. https://doi.org/10.1038/ismej.2015.149 (2016).Article 
    PubMed 

    Google Scholar 
    12.Hermans, D. et al. Colonization factors of Campylobacter jejuni in the chicken gut. Vet. Res. 42, 82. https://doi.org/10.1186/1297-9716-42-82 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    13.Sheppard, S. K. et al. Genome-wide association study identifies vitamin B5 biosynthesis as a host specificity factor in Campylobacter. Proc. Natl. Acad. Sci. U. S. A. 110, 11923–11927. https://doi.org/10.1073/pnas.1305559110 (2013).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.Yahara, K. et al. Genome-wide association of functional traits linked with Campylobacter jejuni survival from farm to fork. Environ. Microbiol. 19, 361–380. https://doi.org/10.1111/1462-2920.13628 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    15.Thépault, A. et al. Genome-wide identification of host-segregating epidemiological markers for source attribution in Campylobacter jejuni. Appl. Environ. Microbiol. 83, e03085-e3116. https://doi.org/10.1128/AEM.03085-16 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Buchanan, C. J. et al. A genome-wide association study to identify diagnostic markers for human pathogenic Campylobacter jejuni strains. Front. Microbiol. 8, 1224. https://doi.org/10.3389/fmicb.2017.01224 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.de Vries, S. P. W. et al. Genome-wide fitness analyses of the foodborne pathogen Campylobacter jejuni in in vitro and in vivo models. Sci. Rep. 7, 1251. https://doi.org/10.1038/s41598-017-01133-4 (2017).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Gormley, F. J. et al. Has retail chicken played a role in the decline of human Campylobacteriosis?. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01455-07 (2008).Article 
    PubMed 

    Google Scholar 
    19.Korczak, B. M., Zurfluh, M., Emler, S., Kuhn-Oertli, J. & Kuhnert, P. Multiplex strategy for multilocus sequence typing, fla typing, and genetic determination of antimicrobial resistance of Campylobacter jejuni and Campylobacter coli isolates collected in Switzerland. J. Clin. Microbiol. https://doi.org/10.1128/JCM.00237-09 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Lévesque, S., Frost, E., Arbeit, R. D. & Michaud, S. Multilocus sequence typing of Campylobacter jejuni isolates from humans, chickens, raw milk, and environmental water in Quebec, Canada. J. Clin. Microbiol. https://doi.org/10.1128/JCM.00042-08 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Habib, I., Uyttendaele, M. & De Zutter, L. Survival of poultry-derived Campylobacter jejuni of multilocus sequence type clonal complexes 21 and 45 under freeze, chill, oxidative, acid and heat stresses. Food Microbiol. 27, 829–834. https://doi.org/10.1016/j.fm.2010.04.009 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    22.Alter, T. & Scherer, K. Stress response of Campylobacter spp. and its role in food processing. J. Vet. Med. Ser. B 53, 351–357. https://doi.org/10.1111/j.1439-0450.2006.00983.x (2006).Article 

    Google Scholar 
    23.Murphy, C., Carroll, C. & Jordan, K. N. Environmental survival mechanisms of the foodborne pathogen Campylobacter jejuni. J. Appl. Microbiol. 100, 623–632. https://doi.org/10.1111/j.1365-2672.2006.02903.x (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    24.Mourkas, E. et al. Agricultural intensification and the evolution of host specialism in the enteric pathogen Campylobacter jejuni. Proc. Natl. Acad. Sci. 117, 11018–11028. https://doi.org/10.1073/pnas.1917168117 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    25.Lees, J. A., Galardini, M., Bentley, S. D., Weiser, J. N. & Corander, J. pyseer: A comprehensive tool for microbial pangenome-wide association studies. Bioinformatics 34, 4310–4312. https://doi.org/10.1093/bioinformatics/bty539 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Schröder, G. & Lanka, E. TraG-like proteins of type IV secretion systems: Functional dissection of the multiple activities of TraG (RP4) and TrwB (R388). J. Bacteriol. 185, 4371–4381. https://doi.org/10.1128/JB.185.15.4371-4381.2003 (2003).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Poly, F., Threadgill, D. & Stintzi, A. Genomic diversity in Campylobacter jejuni: Identification of C. jejuni 81–176-specific genes. J. Clin. Microbiol. 43, 2330–2338. https://doi.org/10.1128/JCM.43.5.2330-2338.2005 (2005).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Lee, K.-Y. et al. Structure-based functional identification of Helicobacter pylori HP0268 as a nuclease with both DNA nicking and RNase activities. Nucleic Acids Res. 43, 5194–5207. https://doi.org/10.1093/nar/gkv348 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    29.Sheppard, S. K., Guttman, D. S. & Fitzgerald, J. R. Population genomics of bacterial host adaptation. Nat. Rev. Genet. 19, 549–565. https://doi.org/10.1038/s41576-018-0032-z (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    30.Sheppard, S. K. et al. Cryptic ecology among host generalist Campylobacter jejuni in domestic animals. Mol. Ecol. 23, 2442–2451. https://doi.org/10.1111/mec.12742 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Mohan, V. et al. Campylobacter jejuni colonization and population structure in urban populations of ducks and starlings in New Zealand. Microbiologyopen 2, 659–673. https://doi.org/10.1002/mbo3.102 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Dingle, K. E. et al. Multilocus sequence typing system for Campylobacter jejuni. J. Clin. Microbiol. 39, 14–23. https://doi.org/10.1128/JCM.39.1.14-23.2001 (2001).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Hershberg, R. Mutation—The engine of evolution: Studying mutation and its role in the evolution of bacteria: Figure 1. Cold Spring Harb. Perspect. Biol. 7, a018077. https://doi.org/10.1101/cshperspect.a018077 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Falush, D. Bacterial genomics: Microbial GWAS coming of age. Nat. Microbiol. 1, 16059. https://doi.org/10.1038/nmicrobiol.2016.59 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    35.Power, R. A., Parkhill, J. & de Oliveira, T. Microbial genome-wide association studies: Lessons from human GWAS. Nat. Rev. Genet. 18, 41–50. https://doi.org/10.1038/nrg.2016.132 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    36.Brandley, M. C., Warren, D. L., Leaché, A. D. & McGuire, J. A. Homoplasy and clade support. Syst. Biol. 58, 184–198. https://doi.org/10.1093/sysbio/syp019 (2009).Article 
    PubMed 

    Google Scholar 
    37.Hassanin, A., Lecointre, G. & Tillier, S. The ‘evolutionary signal’ of homoplasy in proteincoding gene sequences and its consequences for a priori weighting in phylogeny. C. R. l’Acad. Sci. Ser. III Sci. Vie 321, 611–620. https://doi.org/10.1016/S0764-4469(98)80464-2 (1998).CAS 
    Article 

    Google Scholar 
    38.Sheppard, S. K. & Maiden, M. C. J. The evolution of Campylobacter jejuni and Campylobacter coli. Cold Spring Harb. Perspect. Biol. 7, a018119. https://doi.org/10.1101/cshperspect.a018119 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Motiejūnaitė, R., Armalytė, J., Markuckas, A. & Sužiedėlienė, E. Escherichia coli dinJ-yafQ genes act as a toxin-antitoxin module. FEMS Microbiol. Lett. 268, 112–119. https://doi.org/10.1111/j.1574-6968.2006.00563.x (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    40.Buts, L., Lah, J., Dao-Thi, M.-H., Wyns, L. & Loris, R. Toxin–antitoxin modules as bacterial metabolic stress managers. Trends Biochem. Sci. 30, 672–679. https://doi.org/10.1016/j.tibs.2005.10.004 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    41.Gerdes, K., Christensen, S. K. & Løbner-Olesen, A. Prokaryotic toxin–antitoxin stress response loci. Nat. Rev. Microbiol. 3, 371–382. https://doi.org/10.1038/nrmicro1147 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    42.Han, Z. et al. Influence of the gut microbiota composition on Campylobacter jejuni colonization in chickens. Infect. Immun. https://doi.org/10.1128/IAI.00380-17 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Indikova, I., Humphrey, T. J. & Hilbert, F. Survival with a helping hand: Campylobacter and Microbiota. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.01266 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Fijalkowska, I. J., Schaaper, R. M. & Jonczyk, P. DNA replication fidelity in Escherichia coli : A multi-DNA polymerase affair. FEMS Microbiol. Rev. 36, 1105–1121. https://doi.org/10.1111/j.1574-6976.2012.00338.x (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Vandewiele, D., Fernández de Henestrosa, A. R., Timms, A. R., Bridges, B. A. & Woodgate, R. Sequence analysis and phenotypes of five temperature sensitive mutator alleles of dnaE, encoding modified α-catalytic subunits of Escherichia coli DNA polymerase III holoenzyme. Mutat. Res. Mol. Mech. Mutagen. 499, 85–95. https://doi.org/10.1016/S0027-5107(01)00268-8 (2002).CAS 
    Article 

    Google Scholar 
    46.Shan, S., Stroud, R. M. & Walter, P. Mechanism of association and reciprocal activation of two GTPases. PLoS Biol. 2, e320. https://doi.org/10.1371/journal.pbio.0020320 (2004).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Yosef, I., Bochkareva, E. S. & Bibi, E. Escherichia coli SRP, its protein subunit Ffh, and the Ffh M domain are able to selectively limit membrane protein expression when overexpressed. MBio https://doi.org/10.1128/mBio.00020-10 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.Balaban, M., Joslin, S. N. & Hendrixson, D. R. FlhF and its GTPase activity are required for distinct processes in flagellar gene regulation and biosynthesis in Campylobacter jejuni. J. Bacteriol. 191, 6602–6611. https://doi.org/10.1128/JB.00884-09 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Budroni, S. et al. Neisseria meningitidis is structured in clades associated with restriction modification systems that modulate homologous recombination. Proc. Natl. Acad. Sci. 108, 4494–4499. https://doi.org/10.1073/pnas.1019751108 (2011).ADS 
    Article 
    PubMed 

    Google Scholar 
    50.McCarthy, N. D. et al. Host-associated genetic import in Campylobacter jejuni. Emerg. Infect. Dis. 13, 267–272. https://doi.org/10.3201/eid1302.060620 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Asakura, H. et al. Molecular evidence for the thriving of Campylobacter jejuni ST-4526 in Japan. PLoS ONE 7, e48394. https://doi.org/10.1371/journal.pone.0048394 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Morley, L. et al. Gene loss and lineage-specific restriction-modification systems associated with niche differentiation in the Campylobacter jejuni sequence type 403 clonal complex. Appl. Environ. Microbiol. 81, 3641–3647. https://doi.org/10.1128/AEM.00546-15 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.National Research Council. Nutrient Requirements of Swine. Nutrient Requirements of Swine. https://doi.org/10.17226/13298 (National Academies Press, 2012).
    Google Scholar 
    54.Schröder, G. et al. TraG-like proteins of DNA transfer systems and of the Helicobacter pylori type IV secretion system: Inner membrane gate for exported substrates?. J. Bacteriol. 184, 2767–2779. https://doi.org/10.1128/JB.184.10.2767-2779.2002 (2002).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Kienesberger, S. et al. Interbacterial macromolecular transfer by the Campylobacter fetus subsp. venerealis type IV secretion system. J. Bacteriol. 193, 744–758. https://doi.org/10.1128/JB.00798-10 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    56.Velayudhan, J. & Kelly, D. J. Analysis of gluconeogenic and anaplerotic enzymes in Campylobacter jejuni: An essential role for phosphoenolpyruvate carboxykinase. Microbiology 148, 685–694. https://doi.org/10.1099/00221287-148-3-685 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    57.Korczak, B. M. et al. Genetic relatedness within the genus Campylobacter inferred from rpoB sequences. Int. J. Syst. Evol. Microbiol. 56, 937–945. https://doi.org/10.1099/ijs.0.64109-0 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    58.González-González, A., Hug, S. M., Rodríguez-Verdugo, A., Patel, J. S. & Gaut, B. S. Adaptive mutations in RNA polymerase and the transcriptional terminator rho have similar effects on Escherichia coli gene expression. Mol. Biol. Evol. 34, 2839–2855. https://doi.org/10.1093/molbev/msx216 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Richards, S. A. The significance of changes in the temperature of the skin and body core of the chicken in the regulation of heat loss. J. Physiol. 216, 1–10. https://doi.org/10.1113/jphysiol.1971.sp009505 (1971).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Hottes, A. K. et al. Bacterial adaptation through loss of function. PLoS Genet. 9, e1003617. https://doi.org/10.1371/journal.pgen.1003617 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.Iranzo, J., Wolf, Y. I., Koonin, E. V. & Sela, I. Gene gain and loss push prokaryotes beyond the homologous recombination barrier and accelerate genome sequence divergence. Nat. Commun. 10, 5376. https://doi.org/10.1038/s41467-019-13429-2 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Riedel, C. et al. Differences in the transcriptomic response of Campylobacter coli and Campylobacter lari to heat stress. Front. Microbiol. 11. https://doi.org/10.3389/fmicb.2020.00523 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Epping, L. et al. Comparison of different technologies for the decipherment of the whole genome sequence of Campylobacter jejuni BfR-CA-14430. Gut Pathog. 11, 59. https://doi.org/10.1186/s13099-019-0340-7 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Roehr, J. T., Dieterich, C. & Reinert, K. Flexbar 3.0—SIMD and multicore parallelization. Bioinformatics 33, 2941–2942. https://doi.org/10.1093/bioinformatics/btx330 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    65.Nikolenko, S. I., Korobeynikov, A. I. & Alekseyev, M. A. BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics 14(Suppl 1), S7. https://doi.org/10.1186/1471-2164-14-S1-S7 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Bankevich, A. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477. https://doi.org/10.1089/cmb.2012.0021 (2012).MathSciNet 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Seemann, T. Prokka: Rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069. https://doi.org/10.1093/bioinformatics/btu153 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    68.Jolley, K. A. & Maiden, M. C. J. BIGSdb: Scalable analysis of bacterial genome variation at the population level. BMC Bioinform. 11, 595. https://doi.org/10.1186/1471-2105-11-595 (2010).Article 

    Google Scholar 
    69.Zhou, Z. et al. GrapeTree: Visualization of core genomic relationships among 100,000 bacterial pathogens. Genome Res. 28, 1395–1404. https://doi.org/10.1101/gr.232397.117 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Page, A. J. et al. Roary: Rapid large-scale prokaryote pan genome analysis. Bioinformatics 31, 3691–3693. https://doi.org/10.1093/bioinformatics/btv421 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    71.Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313. https://doi.org/10.1093/bioinformatics/btu033 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    72.Tavaré, S. Some probabilistic and statistical problems in the analysis of DNA sequences. Am. Math. Soc. Lect. Math. Life Sci. 17, 57–86 (1986).MathSciNet 
    MATH 

    Google Scholar 
    73.Didelot, X. & Wilson, D. J. ClonalFrameML: Efficient inference of recombination in whole bacterial genomes. PLOS Comput. Biol. 11, e1004041. https://doi.org/10.1371/journal.pcbi.1004041 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Tonkin-Hill, G., Lees, J. A., Bentley, S. D., Frost, S. D. W. W. & Corander, J. RhierBAPs: An R implementation of the population clustering algorithm hierbaps [version 1; referees: 2 approved]. Wellcome Open Res. 3, 93. https://doi.org/10.12688/wellcomeopenres.14694.1 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    75.van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).MATH 

    Google Scholar 
    76.Marttinen, P. et al. Detection of recombination events in bacterial genomes from large population samples. Nucleic Acids Res. 40, e6. https://doi.org/10.1093/nar/gkr928 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    77.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760. https://doi.org/10.1093/bioinformatics/btp324 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    78.Huerta-Cepas, J. et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 44, D286–D293. https://doi.org/10.1093/nar/gkv1248 (2016).CAS 
    Article 

    Google Scholar 
    79.Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122. https://doi.org/10.1093/molbev/msx148 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

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    bric à brac controls sex pheromone choice by male European corn borer moths

    Generation of Resp-and bab-recombinant linesMale informative backcross (BC) families using O. nubilalis Slovenia and Hungary strains15 were generated that exhibited fixed recombination between the flanking genes of the Resp region, trol and not. ZE and EZ hybrid males were backcrossed to a Z-strain female to generate backcross 1 (BC1) (Supplementary Fig. 1a, b). Recombinants between trol and not were identified via polymerase chain reaction (PCR) (Supplementary Methods, Supplementary Table 1), and crossed to Z-strain individuals to obtain BC2 (Supplementary Fig. 1c). BC2 individuals were genotyped to detect recombinants, then mated with each other to generate inbred 1 (IB1) crosses (Supplementary Fig. 1d). IB1 adults with the desired genotype were mass reared to obtain IB2 (Supplementary Fig. 1e). IB2 families that originated from a BC1 male cross were fixed homozygote recombinants, whereas BC1 female cross descendants were genotyped and inbred again to obtain fixed recombinant homozygotes (Supplementary Fig. 1f, g). Nine Resp-recombinant lines had one recombination point between homozygous trol and not genes (L165, L173, L185, L190, L195, L205, L215, L220, L237). bab-recombinant lines exhibited fixed recombination between bab’s flanking genes, ago and not, and were generated using the two homozygote recombinant lines L165 with Z-strain phenotype and L205 with E-strain phenotype. Single pair matings between L165 females and L205 males were set up to obtain hybrid males, which were backcrossed to L165 females. The BC individuals were screened with PCR (Supplementary Methods) to select recombinant adults that were used for inbred mass rearing. The PCR selection process continued until two fixed homozygote populations were established, i.e. line L44-Z and line L44-E (Fig. 2a).Genomic sequencing of Resp-recombinant linesA pool of 10 male pupae of lines L165 and L205 were homogenized in liquid nitrogen using mortar and pestle and DNA extractions were performed with QIAGEN Genomic-tip 100/G and the Genomic DNA Buffer Set (Qiagen, Hilden, Germany) according to the manufacturers’ instructions, but extending incubation times with buffer G2 containing proteinase K and RNase A to 12 h. HMW genomic DNA was sent to GATC Biotech for sequencing. Sequencing was done using an Illumina HiSeq2500 instrument, obtaining ~200 Mio paired end (2 × 150 bp) sequences per Resp-recombinant line. Shotgun genome assemblies were generated using the CLC Genomics Workbench v10.1. For PacBio sequencing, HMW genomic DNA was isolated from individual pupae of lines L165 and L205 by the Max Planck-Genome Centre Cologne (MPGCC) using the Qiagen MagAttract HMW DNA Kit. Sequencing of the size-selected HMW genomic DNA of each strain further purified with AMPure beads was performed at the MPGCC on a PacBio Sequel instrument. PacBio reads for both recombinant lines were assembled separately using the HGAP4 assembly pipeline implemented in the SMRT analysis software with standard settings. After genome sequencing of lines L165 and L205, primers were designed which amplified line-specific size polymorphisms and used to narrow down the breakpoint within all Resp-recombinant lines (Supplementary Methods, Supplementary Table 1).Phenotyping with wind tunnel assaysWind tunnel experiments were conducted with 0–5-day-old unmated males in a 2.5 × 1 × 1 m wind tunnel at 20–25 °C, 70% humidity, 30 cm/s airflow, and 26% red light. Synthetic lures (Z-strain lure: 97% Z11-14:OAc + 3% E11-14:OAc; E-strain lure: 99% E11-14:OAc + 1% Z11-14:OAc) diluted Z11-14:OAc and E11-14:OAc (purity of ≥99%, Pherobank, Wijk bij Duurstede, Netherlands) with hexane to 30 µg per lure. Blend quality and quantity was confirmed with gas chromatography. Pheromones were applied to rubber septa (Thomas Scientific, Swedesboro, NJ, USA) and stored at −20 °C. Individual males were placed in a small cylinder (10 cm, 3.2 cm diameter) covered with netted cloth at both ends permitting flow of odorized air. After placing the cylinder at the downwind end of the wind tunnel, male behavior, i.e. (1) resting (=no response), (2) wing fanning (=medium response), and (3) hair-pencil extrusion (=highest response), was recorded using setup adapted from Koutroumpa et al. 15, Supplementary Fig. 11). Each male was exposed to one blend for 60 s, kept for 30–60 min in the tunnel without any odor, and then the opposite blend was tested. Blends testing order was switched between experimental days. Statistical analysis was performed with R version 3.6.144 using Fisher’s Exact or Chi-squared test. To complement behavioral phenotypes, electrophysiological phenotypes (electroantennogram (EAG) and single sensillum recordings (SSR)) of bab-recombinant and CRISPR lines (described below) were recorded (Supplementary Methods).RNA isoform identificationDe novo transcriptomes of US laboratory populations45 were constructed using Trinity46 separately for E- and Z-strain individuals following methods in Levy et al. 47 to identify all splice variants of candidate genes. RNA was isolated from larval heads45, adult female heads47, or from whole pupae newly reported here. Briefly, RNA was extracted from samples using RNeasy kits (Qiagen, Hilden, Germany), then quantified with a Nanodrop (Thermo Scientific, Wilmington, DE, USA) and Qubit Broad Range RNA assays (Life Technologies, Carlsbad, CA, USA). cDNA libraries were prepared from mRNA using the TruSeq Sample Prep Kit v2 Set A (Illumina Inc., San Diego, CA, USA) using 1 mg total RNA, and prepared libraries were quantified using the Qubit High Sensitivity DNA assay. Libraries were quantified a second time on an Agilent Bioanalyzer (Santa Clara, CA, USA). Libraries were run on an Illumina HiSeq 2500, located at the Tufts University Core Facility for Genomics (Boston, MA, USA) to generate 100 bp single-end reads. Single-end reads were assessed for quality using the FastQC program (http://www.bioinformatics.babraham.ac.uk/projects/fastqc). Sequences were then trimmed using Trimmomatic version 0.35 to remove adapter sequences, bases with low sequence quality, and any reads that were shorter than 36 base pairs. FastQC reports were generated for each file again to confirm post-trimming quality. Mitochondrial DNA and ribosomal RNA sequences were removed using Bowtie248 by aligning against known mtDNA sequences and identical reads were collapsed prior to assembly (but counts retained) using the FastX Toolkit version 0.013 (http://hannonlab.cshl.edu/fastx_toolkit). The transcriptome was assembled de novo using Trinity46 and a k-mer length of 25. The longest transcript for each component were retained using custom scripts.Reverse-transcription quantitative PCR (RT-qPCR)Six genes in the Resp region between kon and not (Bap18, LIM, Bgi-A, Bgi-B, ago, bab), plus Orco and OnubOR6 were analyzed for their expression ratio in different tissues of E-strain and Z-strain individuals of European laboratory populations. Stages and tissues include: 5th instar larvae (antennae, head without antennae, thorax, abdomen), prepupal instar (head, thorax, abdomen), 2- and 4-day-old male and female pupae (antennae), 2-day-old male and female adults (antennae, brain, 1st pair of legs, 2nd plus 3rd pair of legs, abdomen). Expression ratios of bab were additionally evaluated for 7-day-old male and female pupal antennae as well as for 7-day-old male and female antennae and brains. Due to the large number of samples needing to be tested for expression simultaneously, a first qPCR was run comparing all tissues within each strain (Fig. 1a). At a next step only most expressed and most related tissues to the scientific question (i.e., antennae and brain) were included and comparisons were made simultaneously for the two strains (Supplementary Fig. 3). Three biological replicates of each of 27 sample types were collected during the second hour of scotophase from each strain. Total RNAs were extracted from each tissue using a Trizol/Chloroform approach followed by RNeasy Micro Kit purification (QIAGEN). Single-stranded cDNA synthesis was performed from 1 μg total RNA with iScript Reverse Transcription Supermix for RT-qPCR from BioRad (Hercules, CA, USA). Three control genes, (GAPDH, 18S rRNA, rpL8) were tested for stability between samples, and rpL849 was chosen for final comparisons. Gene-specific primers designed using “Primer 3”50 amplified 100–200 bp fragments (Supplementary Table 2). qPCR reactions were performed using Sso Advanced Universal SYBR Green Supermix (BioRad) in a total volume of 12 μl with 3 μl cDNA (or water as negative control or RNA for controlling the absence of genomic DNA) and 0.25 mM of each primer. cDNA amplifications were performed in a BioRad CFX96 Real-Time System using a gradient of annealing temperatures for each gene of interest. Three gradient temperatures were tested per gene on a 4-fold dilution series, 1/4–1/128 of a sample representative cDNA pool [E = 10 (−1/slope)] for relative quantification of the same gene in all other cDNA samples. Two replicates of each dilution were tested. A melting curve ramp (65–95 °C: Increment 0.5 °C/5 s) was generated to confirm that reactions did not produce nonspecific amplification. The final protocol included a denaturation step at 95 °C for 3 min followed by 40 cycles of amplification and quantification (denaturation at 95 °C for 10 s and annealing for 30 s at temperatures given in Supplementary Table 2 for each primer pair). Reactions were performed in two technical replicates. After confirming similar amplification efficiencies of target and control gene, expression levels were calculated relative to rpL8 expression and expressed as the ratio = E(−Cq Resp candidate)/E(−Cq rpL8)51. Statistical comparisons between strains, sexes, and tissues for each gene were assessed using one-way analysis of variance (ANOVA), followed by honest-significant difference (HSD) tests (post hoc Tukey’s test). A Benjamini–Hochberg multiple-test correction was applied over the genes tested.Targeted mutagenesis of bab exon 1.5Nine RNA guides were designed against intron 1A, exon 1.5, and intron 1B of bab (Supplementary Table 3) using the CRISPOR gRNA design tool cripsor.tefor.net and the O. nubilalis bab genomic DNA sequence as target. Guide sequences were subcloned in DR274 (http://www.addgene.org/42250) derived vector. Plasmids were digested by DraI, purified, and transcribed using HiScribe T7 high yield RNA synthesis kit (New England Biolabs). Reactions were purified using EZNA microelute RNA clean-up kit (OMEGA Biotek). Streptococcus pyogenes Cas9 protein, bearing three nuclear localization sequences, was provided by TacGene (Paris-France)52. Nine different guide RNAs were designed; three targeting exon 1.5, three in the preceding intron, and three in the following intron. Aliquots of sgRNA were denatured at 80 °C for 2 min and then left on ice for 2 min before mixing them with the equivalent amount of Cas9 for a sgRNA:Cas9 complex ratio of 1.5:1. Concentrations of the sgRNA are given in Supplementary Table 3 and the Cas9 was 30 µM (Sp-Cas9-NLS-GFP-NLS). The complex was formed at room temperature (RT) for 10 min. sgRNA:Cas9 complexes were formed separately for each sgRNA to ensure that Cas9 would bind equally to each sgRNA. These were combined as desired and placed on ice. Eggs of either strain from the European populations were injected (using an Eppendorf FemtoJet 4i injector) within 0.5 h after oviposition to target the one cell embryo stage. We injected three combinations of sgRNA (Supplementary Table 3) in order to create a deletion 5′ of exon 1.5 (KO1), a deletion 3′ of exon 1.5 (KO2), or a complete deletion of exon 1.5 (DEL). Injected eggs were reared to adulthood and genotyped. DNA of adult legs was extracted51 and amplified with Terra™ PCR Direct Polymerase Mix (Takara Bio Europe) using primer Bab-Z/E-i01-F9 (GTGCATTTCCTGCTTATGA) on intron 1, Bab-E-i01-R10 (AATTTGCCCCTAAGTGTACC) on intron 1.5, and the following program: 98 °C for 2 min, 35×(10 s at 98 °C, 15 s at 60 °C, 30 s at 68 °C). Size polymorphism were detected with agarose gel analysis and confirmed by Sanger sequencing (Macrogen, Amsterdam). Sequences were aligned using SEQUENCHER™ 4.7 (Gene Codes Corporation, Inc.). Heterozygote G0 adults with mutations were crossed to adults from the wild type rearing. G1 heterozygote males and females carrying the same mutation were crossed to obtain homozygote G2 mutants. Four G2 CRISPR lines were established: lines L46 (KO1), L72α (KO2), L72β (KO2), and L73 (KO2). Males of all CRISPR lines were phenotyped using EAG (Supplementary Methods) and wind tunnel assays.Whole mount in situ hybridizationMale O. nubilalis whole antennae were mounted and in situ hybridized with two RNA probes simultaneously. bab digoxigenin-labeled antisense riboprobe, was generated using a Sp6/T7 RNA transcription system (Roche) and linearized recombinant pCRII-TOPO plasmids (TOPO TA cloning kit Invitrogen) following manufacturer’s protocols. Orco, OR4, OR6, and OR7 probes are the same preparations that were used in ref. 21. Two color double in situ hybridization with two different antisense RNA probes (digoxigenin-labeled or biotin-labeled probes), as well as visualization of hybridization were performed as reported previously21,53 and described below. Antennae of 1–2-day-old Z-strain and E-strain male moths from the European laboratory populations were dissected by first cutting off the tips. The remaining antennal stem was further cut into smaller pieces of 5–15 antennal segments. The same procedure was done for 4-day-old pupal antennae that were extracted underneath the pupal cuticle, which was broken and lifted at antennal base so that the antenna could be pulled out with forceps.DIG-labeled probes were detected by an anti-DIG AP-conjugated antibody in combination with HNPP/Fast Red (Fluorescent detection Set; Roche); for biotin-labeled probes the TSA kit (Perkin Elmer, Boston, MA, USA), including an antibiotin–streptavidin–horseradish peroxidase conjugate and FITC tyramides as substrate was used. All incubations and washes were made in a volume of 0.3 mL (unless otherwise stated) in 0.5 mL tubes with slow rotation on a small table rotor at RT or in a hybridization oven (Bambino, Dutcher) when heating was needed. Antennal fragments were fixed in 4% paraformaldehyde in 0.1 M NaCO3, pH 9.5 for 24 h at 4 °C (PF1) followed by washes at RT for 1 min in phosphate-buffered saline (PBS: 0.85% NaCl, 1.4 mM KH2PO4, 8 mM Na2HPO4, pH 7.1), 10 min in 0.2 M HCl and 2 min in PBS with 1% Triton X-100. Antennal fragments were then incubated for 3 h in whole mount hybridization solution (50% formamide, 1% Tween 20, 0.1% CHAPS, 50 µg/mL yeast tRNA, 5× SSC, 1× Denhart’s reagent and 5 mM EDTA, pH 8.0) at 55 °C. Hybridization, using one DIG-labeled and one biotin-labeled probe, took place at 55 °C. Prior to hybridization, probes were diluted to adequate ratios (final volume 200 µL) in hybridization buffer (50% formamide, 10% dextran sulfate, 2× SSC, 0.2 µg/µL yeast tRNA, 0.2 µg/µL herring sperm DNA) and heated for 10 min at 65 °C. After heating, the probes were kept on ice for at least 5 min before use. Post-hybridization antennal fragments were washed four times for 15 min in 200 µL of 0.1× SSC (1× SSC = 0.15 M NaCl, 0.015 M Na-citrate, pH 7.0) at 60 °C then treated for 16 h in 5 mL of blocking solution (10 g blocking reagent from Roche in up to 100 mL maleic acid solution: 0.1 mol/L maleic acid and 0.15 mol/L NaCl) in 45 mL TBS and 150 µL Triton X-100 at 4 °C. The next step was to incubate fragments for 48 h with an anti-dioxigenin alkaline phosphatase-conjugated antibody (Roche) diluted 1:500 and with a streptavidine horse radish peroxidase-conjugate diluted 5:500 in blocking solution in TBS prepared as previously. After washing five times for 10 min in TBS, 0.05% Tween, antennal fragments were rinsed in DAP-buffer (100 mM Tris, pH 9.5, 100 mM NaCl, 50 mM MgCl2), after which hybridization signals were visualized using HNPP (Roche; 1:100 in DAP-buffer, pH 8.0) incubations for 15 h at 4 °C. After washing five times for 10 min in TBS, 0.05% Tween, antennal fragments were incubated for 18 h with the TSA kit substrates (Perkin Elmer, MA, USA): 2% Tyramide in amplification diluent. After a last set of washes, five times for 10 min in TBS, 0.05% Tween, antennal fragments were mounted in 1/3 PBS/glycerol and specific antennal cell stainings were observed with a Zeiss (Oberkochen, Germany) LSM 700 confocal laser scanning microscope (MIMA2 Platform, INRA, France, https://doi.org/10.15454/1.5572348210007727E12). Images were arranged in Powerpoint (Microsoft) and Adobe Illustrator (Adobesystems, San Jose, CA, USA) and were not altered except adjusting brightness or contrast for uniform tone within a figure.Phenotyping pheromone preference in naturePheromone trapping in North America was used to collect wild E-pheromone and Z-pheromone preferring males using Scentry Heliothis traps baited with synthetic E (“New York”) and Z (“Iowa”) lures (Scentry Biologicals, Billings, MO, USA). Traps were placed directly next to sweet corn fields and males were collected from each trap every 1–2 weeks and stored at −20 °C. Lures were replaced every 2 weeks. Trapping of >20 males from each E and Z trap was done at three sympatric sites between 2010 and 2012 (Supplementary Table 4). Tissues were moved from −20 °C within 3 months of collection to at −80 °C for long-term storage. DNA was isolated from both Pennsylvania sites by grinding frozen tissues and using the Qiagen DNeasy tissue protocol (Qiagen, Germantown, MD, USA) without vortexing preserve high molecular weight DNA. DNA isolation of samples from Bellona, NY was conducted with Qiagen genomic tips (20 G). All samples were treated with Qiagen RNase. DNA concentrations were quantified using Qubit prior to sequencing.Individual genome resequencing of field mothsIndividual resequencing data were collected for 31 E-trapped and 31 Z-trapped individuals from two sites (Rockspring, PA, USA (n = 15 per trap), and Landisville, PA, USA (n = 16 per trap); Supplementary Table 5). Landisville, PA, Z-trap data were originally described by Kozak et al. 54; all other data are new. Libraries were prepared using Illumina TruSeq (Illumina Inc.) and sequenced on an Illumina NextSeq using 150 bp paired-end sequencing at Cornell University. Trimmed genomic data were analyzed using the GATK best practices pipeline55,56,57 with data aligned to the repeat-masked genome reference (GenBank BioProject: PRJNA534504; Accession SWFO0000000054) using bwa58, sorted and filtered using Picard and samtools to remove duplicates and reads with a mapping quality score below 20. SNPs and small indels were called using GATK Haplotype caller (joint genotyping mode) after realigning around indels and filtered using recommended GATK filters57. Large structural variants (SV) were called from aligned bam files using information from split paired end reads using split reads and anomalies in pair orientation and insert size in Delly259 (https://github.com/dellytools/delly); these structural variants included indels ( >300 bp), translocations, and inversions. Delly2 was run on all individual files, these were merged to a consensus SV file and genotypes were reassessed.BayPASS 2.160 was used to identify SNPs associated with pheromone trap while controlling for population demography in the individual resequencing data using allele frequencies for our four populations to test the association with pheromone trap (Z = 1, E = −1) using the STD model. As described in Kozak et al. 54, significantly associated polymorphisms had XtX above the 0.001% quantile of pseudo-observed data of simulated “neutral” loci, BF  > 20 dB61, and eBPis  > 2 (equivalent to P value  More

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    Oil palm cultivation critically affects sociality in a threatened Malaysian primate

    1.Rosa, I. M. D., Smith, M. J., Wearn, O. R., Purves, D. & Ewers, R. M. The environmental legacy of modern tropical deforestation. Curr. Biol. 26, 2161–2166 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.FAO. Global Forest Resources Assessment 2020—Key findings. https://doi.org/10.4060/ca8753en. (Accessed July 20, 2020).3.Sih, A., Ferrari, M. C. O. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Fetene, A., Yeshitela, K. & Gebremariam, E. The effects of anthropogenic landscape change on the abundance and habitat use of terrestrial large mammals of Nech Sar National Park. Environ. Syst. Res. 8, 19 (2019).Article 

    Google Scholar 
    5.Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Peres, C. A. Synergistic effects of subsistence hunting and habitat fragmentation on Amazonian forest vertebrates. Conserv. Biol. 15, 1490–1505 (2001).Article 

    Google Scholar 
    7.Estrada, A., Raboy, B. E. & Oliveira, L. C. Agroecosystems and primate conservation in the tropics: A review. Am. J. Primatol. 74, 696–711 (2012).PubMed 
    Article 

    Google Scholar 
    8.Azhar, B. et al. Contribution of illegal hunting, culling of pest species, road accidents and feral dogs to biodiversity loss in established oil-palm landscapes. Wildl. Res. 40, 1–9 (2012).Article 

    Google Scholar 
    9.IUCN. The IUCN Red List of Threatened Species. https://www.iucnredlist.org/en. (Accessed July 19, 2020).10.Van Buskirk, J. Behavioural plasticity and environmental change. In Behavioural Responses to a Changing World: Mechanisms and Consequences (eds. Candolin, U. & Wong, B. B. M.) 145–158 (Oxford University Press, 2012).11.McLennan, M. R., Spagnoletti, N. & Hockings, K. J. The implications of primate behavioral flexibility for sustainable human-primate coexistence in anthropogenic habitats. Int. J. Primatol. 38, 105–121 (2017).Article 

    Google Scholar 
    12.Ward, A. & Webster, M. Sociality: The Behaviour of Group-Living Animals. (Springer International Publishing, 2016).13.Schülke, O. & Ostner, J. Ecological and social influences on sociality. In The Evolution of Primate Societies (eds. Mitani, J. C. et al.) 193–219 (University of Chicago Press, 2012).14.Young, C., Majolo, B., Heistermann, M., Schülke, O. & Ostner, J. Responses to social and environmental stress are attenuated by strong male bonds in wild macaques. PNAS 111, 18195–18200 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    15.McFarland, R. & Majolo, B. Coping with the cold: Predictors of survival in wild Barbary macaques, Macaca sylvanus. Biol. Lett. 9, 20130428 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Schülke, O., Bhagavatula, J., Vigilant, L. & Ostner, J. Social bonds enhance reproductive success in male macaques. Curr. Biol. 20, 2207–2210 (2010).PubMed 
    Article 
    CAS 

    Google Scholar 
    17.Kulik, L., Muniz, L., Mundry, R. & Widdig, A. Patterns of interventions and the effect of coalitions and sociality on male fitness. Mol. Ecol. 21, 699–714 (2012).PubMed 
    Article 

    Google Scholar 
    18.Silk, J. B. et al. Strong and consistent social bonds enhance the longevity of female baboons. Curr. Biol. 20, 1359–1361 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    19.Silk, J. B. et al. The benefits of social capital: Close social bonds among female baboons enhance offspring survival. Proc. R. Soc. B 276, 3099–3104 (2009).PubMed 
    Article 

    Google Scholar 
    20.Brent, L. J. N., Lehmann, J. & Ramos-Fernández, G. Social network analysis in the study of nonhuman primates: A historical perspective. Am. J. Primatol. 73, 720–730 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Henzi, S. P. & Barrett, L. The value of grooming to female primates. Primates 40, 47–59 (1999).Article 

    Google Scholar 
    22.Sueur, C., Jacobs, A., Amblard, F., Petit, O. & King, A. J. How can social network analysis improve the study of primate behavior?. Am. J. Primatol. 73, 703–719 (2011).PubMed 
    Article 

    Google Scholar 
    23.Palagi, E. Not just for fun! Social play as a springboard for adult social competence in human and non-human primates. Behav. Ecol. Sociobiol. 72, 90 (2018).Article 

    Google Scholar 
    24.Amici, F., Kulik, L., Langos, D. & Widdig, A. Growing into adulthood—A review on sex differences in the development of sociality across macaques. Behav. Ecol. Sociobiol. 73, 18 (2019).Article 

    Google Scholar 
    25.Vandeleest, J. J. et al. Decoupling social status and status certainty effects on health in macaques: A network approach. PeerJ 4, e2394 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Lehmann, J., Majolo, B. & McFarland, R. The effects of social network position on the survival of wild Barbary macaques, Macaca sylvanus. Behav. Ecol. 27, 20–28 (2016).Article 

    Google Scholar 
    27.Maestripieri, D. Maternal influences on primate social development. Behav. Ecol. Sociobiol. 72, 130 (2018).Article 

    Google Scholar 
    28.Maestripieri, D. Social and demographic influences on mothering style in pigtail macaques. Ethology 104, 379–385 (1998).Article 

    Google Scholar 
    29.Fairbanks, L. A. Individual differences in maternal style: Causes and consequences for mothers and offspring. Adv. Stud. Behav. 25, 579–611 (1996).Article 

    Google Scholar 
    30.Kulik, L., Langos, D. & Widdig, A. Mothers make a difference: Mothers develop weaker bonds with immature sons than daughters. PLoS ONE 11, e0154845 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    31.Thierry, B. Social epigenesis. In Macaque Societies. A Model for the Study of Social Organization (eds. Thierry, B. et al.) 267–289 (Cambridge University Press, 2004).32.Kaufman, I. C. & Rosenblum, L. A. The waning of the mother–infant bond in two species of macaque. In Determinants of Infant Behavior (ed. Foss, B. M.) vol. IV 41–59 (Metheun, 1969).33.Balasubramaniam, K. N. et al. Impact of individual demographic and social factors on human-wildlife interactions: A comparative study of three macaque species. Sci. Rep. 10, 21991 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.el Alami, A., Lavieren, E. V., Rachida, A. & Chait, A. Differences in activity budgets and diet between semiprovisioned and wild-feeding groups of the endangered Barbary macaque (Macaca sylvanus) in the Central High Atlas Mountains, Morocco. Am. J. Primatol. 74, 210–216 (2012).PubMed 
    Article 

    Google Scholar 
    35.Koirala, S. et al. Diet and activity of Macaca assamensis in wild and semi-provisioned groups in Shivapuri Nagarjun National Park, Nepal. Folia Primatol. 88, 57–74 (2017).Article 

    Google Scholar 
    36.Balasubramaniam, K. N. et al. Impact of anthropogenic factors on affiliative behaviors among bonnet macaques. Am. J. Phys. Anthropol. 171, 704–717 (2020).PubMed 
    Article 

    Google Scholar 
    37.Kaburu, S. S. K. et al. Interactions with humans impose time constraints on urban-dwelling rhesus macaques (Macaca mulatta). Behaviour 156, 1255–1282 (2019).Article 

    Google Scholar 
    38.Marty, P. R. et al. Time constraints imposed by anthropogenic environments alter social behaviour in long-tailed macaques. Anim. Behav. 150, 157–165 (2019).Article 

    Google Scholar 
    39.Meijaard, E. et al. Oil Palm and Biodiversity: A Situation Analysis by the IUCN Oil Palm Task Force (IUCN, 2018).Book 

    Google Scholar 
    40.Ruppert, N., Holzner, A., See, K. W., Gisbrecht, A. & Beck, A. Activity budgets and habitat use of wild southern pig-tailed macaques (Macaca nemestrina) in oil palm plantation and forest. Int. J. Primatol. 39, 237–251 (2018).Article 

    Google Scholar 
    41.Holzner, A. et al. Macaques can contribute to greener practices in oil palm plantations when used as biological pest control. Curr. Biol. 29, R1066–R1067 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    42.Bernstein, I. S. A field study of the pigtail monkey (Macaca nemestrina). Primates 8, 217–228 (1967).Article 

    Google Scholar 
    43.Barrett, L., Gaynor, D. & Henzi, S. P. A dynamic interaction between aggression and grooming reciprocity among female chacma baboons. Anim. Behav. 63, 1047–1053 (2002).Article 

    Google Scholar 
    44.Balasubramaniam, K. N., Berman, C. M., Ogawa, H. & Li, J. Using biological markets principles to examine patterns of grooming exchange in Macaca thibetana. Am. J. Primatol. 73, 1269–1279 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    45.Caldecott, J. O. An Ecological and Behavioural Study of the Pig-Tailed Macaque. (S. Karger, 1986).46.Ciani, A. C. Intertroop agonistic behavior of a feral rhesus macaque troop ranging in town and forest areas in India. Aggress. Behav. 12, 433–439 (1986).Article 

    Google Scholar 
    47.Williams, S. M. & Lindell, C. A. The influence of a single species on the space use of mixed-species flocks in Amazonian Peru. Mov. Ecol. 7, 37 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Martínez, A. E., Gomez, J. P., Ponciano, J. M. & Robinson, S. K. Functional traits, flocking propensity, and perceived predation risk in an Amazonian understory bird community. Am. Nat. 187, 607–619 (2016).PubMed 
    Article 

    Google Scholar 
    49.Southwick, C. H., Siddioi, M. F., Farooqui, M. Y. & Pal, B. C. Effects of artificial feeding on aggressive behaviour of rhesus monkeys in India. Anim. Behav. 24, 11–15 (1976).CAS 
    PubMed 
    Article 

    Google Scholar 
    50.Bonnell, T. R., Vilette, C., Young, C., Henzi, S. P. & Barrett, L. Formidable females redux: male social integration into female networks and the value of dynamic multilayer networks. Curr. Zool. 67, 49–57 (2020).51.Brent, L. J. N. Friends of friends: Are indirect connections in social networks important to animal behaviour?. Anim. Behav. 103, 211–222 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Balasubramaniam, K., Beisner, B., Vandeleest, J., Atwill, E. & McCowan, B. Social buffering and contact transmission: Network connections have beneficial and detrimental effects on Shigella infection risk among captive rhesus macaques. PeerJ 4, 2630 (2016).Article 

    Google Scholar 
    53.Morrow, K. S., Glanz, H., Ngakan, P. O. & Riley, E. P. Interactions with humans are jointly influenced by life history stage and social network factors and reduce group cohesion in moor macaques (Macaca maura). Sci. Rep. 9, 20162 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Snijders, L., Blumstein, D. T., Stanley, C. R. & Franks, D. W. Animal social network theory can help wildlife conservation. Trends Ecol. Evol. 32, 567–577 (2017).PubMed 
    Article 

    Google Scholar 
    55.Johnson, R. L. Mother–infant contact and maternal maintenance activities among free-ranging rhesus monkeys. Primates 27, 191–203 (1986).Article 

    Google Scholar 
    56.Karssemeijer, G. J., Vos, D. R. & van Hooff, J. A. R. A. M. The effect of some non-social factors on mother–infant contact in long-tailed macaques (Macaca fascicularis). Behaviour 113, 273–291 (1990).Article 

    Google Scholar 
    57.Gumert, M. D. Grooming and infant handling interchange in Macaca fascicularis: The relationship between infant supply and grooming payment. Int. J. Primatol. 28, 1059–1074 (2007).Article 

    Google Scholar 
    58.Maestripieri, D. Mother–infant relationships in three species of macaques (Macaca mulatta, M. nemestrina, M. arctoides). I. Development of the mother–infant relationship in the first three months. Behaviour 131, 75–96 (1994).Article 

    Google Scholar 
    59.Gazagne, E. et al. Northern pigtailed macaques rely on old growth plantations to offset low fruit availability in a degraded forest fragment. Am. J. Primatol. 82, e23117 (2020).PubMed 
    Article 

    Google Scholar 
    60.Behie, A. M., Pavelka, M. S. M. & Chapman, C. A. Sources of variation in fecal cortisol levels in howler monkeys in Belize. Am. J. Primatol. 72, 600–606 (2010).PubMed 

    Google Scholar 
    61.Ellis, S., Snyder-Mackler, N., Ruiz-Lambides, A., Platt, M. L. & Brent, L. J. N. Deconstructing sociality: The types of social connections that predict longevity in a group-living primate. Proc. R. Soc. B 286, 20191991 (2019).PubMed 
    Article 

    Google Scholar 
    62.Shutt, K., MacLarnon, A., Heistermann, M. & Semple, S. Grooming in Barbary macaques: Better to give than to receive?. Biol. Lett. 3, 231–233 (2007).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Belton, L. E., Cameron, E. Z. & Dalerum, F. Social networks of spotted hyaenas in areas of contrasting human activity and infrastructure. Anim. Behav. 135, 13–23 (2018).Article 

    Google Scholar 
    64.Testard, C. et al. Rhesus macaques build new social connections after a natural disaster. Curr. Biol. https://doi.org/10.1016/j.cub.2021.03.029 (2021).Article 
    PubMed 

    Google Scholar 
    65.Schino, G. Grooming, competition and social rank among female primates: A meta-analysis. Anim. Behav. 62, 265–271 (2001).Article 

    Google Scholar 
    66.Wooddell, L. J., Kaburu, S. S. K. & Dettmer, A. M. Dominance rank predicts social network position across developmental stages in rhesus monkeys. Am. J. Primatol. 82, 23024 (2019).
    Google Scholar 
    67.Cords, M. Post-conflict reunions and reconciliation in long-tailed macaques. Anim. Behav. 44, 57–61 (1992).Article 

    Google Scholar 
    68.Sosa, S. The influence of gender, age, matriline and hierarchical rank on individual social position, role and interactional patterns in Macaca sylvanus at ‘La Forêt des Singes’: A multilevel social network approach. Front. Psychol. 7, 529 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    69.Dunayer, E. S. & Berman, C. M. Infant handling among primates. Int. J. Comp. Psychol. 31, 1–32 (2018).Article 

    Google Scholar 
    70.Prescott, M. J., Nixon, M. E., Farningham, D. A. H., Naiken, S. & Griffiths, M.-A. Laboratory macaques: When to wean?. Appl. Anim. Behav. Sci. 137, 194–207 (2012).Article 

    Google Scholar 
    71.Lancaster, J. B. Play-mothering: The relations between juvenile females and young infants among free-ranging vervet monkeys (Cercopithecus aethiops). Folia Primatol. 15, 163–182 (1971).CAS 
    Article 

    Google Scholar 
    72.Maestripieri, D. Social structure, infant handling, and mothering styles in group-living old world monkeys. Int. J. Primatol. 15, 531–553 (1994).Article 

    Google Scholar 
    73.Engelhardt, A. & Perwitasari-Farajallah, D. Reproductive biology of Sulawesi crested black macaques (Macaca nigra). Folia Primatol. 79, 326 (2008).
    Google Scholar 
    74.Takahata, Y. et al. Does troop size of wild Japanese macaques influence birth rate and infant mortality in the absence of predators?. Primates 39, 245–251 (1998).Article 

    Google Scholar 
    75.Krishna, B. A., Singh, M. & Singh, M. Population dynamics of a group of lion-tailed macaques (Macaca silenus) inhabiting a rainforest fragment in the Western Ghats, India. Folia Primatol. 77, 377–386 (2006).CAS 
    Article 

    Google Scholar 
    76.Okamoto, K., Matsumura, S. & Watanabe, K. Life history and demography of wild moor macaques (Macaca maurus): Summary of ten years of observations. Am. J. Primatol. 52, 1–11 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    77.Santosa, Y. Determination of long-tailed macaque’s (Macaca fascicularis) harvesting quotas based on demographic parameters. Biodiversitas 13, 79–85 (2012).Article 

    Google Scholar 
    78.Fürtbauer, I., Schülke, O., Heistermann, M. & Ostner, J. Reproductive and life history parameters of wild female Macaca assamensis. Int. J. Primatol. 31, 501–517 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    79.Marshall, A. J. & Wich, S. A. Why conserve primates? In An Introduction to Primate Conservation (eds. Wich, S. A. & Marshall, A. J.) 13–30 (Oxford University Press, 2016).80.Greenwood, P. J. Mating systems, philopatry and dispersal in birds and mammals. Anim. Behav. 28, 1140–1162 (1980).Article 

    Google Scholar 
    81.Kark, S. Effects of ecotones on biodiversity. In Encyclopedia of Biodiversity (ed. Levin, S. A.) (Elsevier, 2013).82.Altmann, J. Observational study of behavior: Sampling methods. Behaviour 49, 227–267 (1974).CAS 
    PubMed 
    Article 

    Google Scholar 
    83.Thierry, B. et al. The social repertoire of Sulawesi macaques. Primate Res. 16, 203–226 (2000).Article 

    Google Scholar 
    84.Widdig, A., Nürnberg, P., Krawczak, M., Streich, W. J. & Bercovitch, F. B. Affiliation and aggression among adult female rhesus macaques: A genetic analysis of paternal cohorts. Behaviour 139, 371–391 (2002).Article 

    Google Scholar 
    85.Silverman, B. W. Density Estimation for Statistics and Data Analysis. Monographs on Statistics and Applied Probability. vol. 26 (Chapman and Hall, 1988).86.Steiniger, S. & Hunter, A. J. S. OpenJUMP HoRAE – A free GIS and toolbox for home-range analysis. Wildl. Soc. Bull. 36, 600–608 (2012).87.The JUMP Pilot Project. OpenJUMP GIS—The free, java-based open source GIS. http://www.openjump.org. (Accessed February 26, 2021).88.QGIS Development Team. QGIS Geographic Information System. (Open Source Geospatial Foundation, 2020).89.David, H. A. Ranking from unbalanced paired-comparison data. Biometrika 74, 432–436 (1987).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    90.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2018).91.Neumann, C. et al. Assessing dominance hierarchies: Validation and advantages of progressive evaluation with Elo-rating. Anim. Behav. 82, 911–921 (2011).Article 

    Google Scholar 
    92.de Vries, H., Stevens, J. M. G. & Vervaecke, H. Measuring and testing the steepness of dominance hierarchies. Anim. Behav. 71, 585–592 (2006).Article 

    Google Scholar 
    93.Bernstein, I. S. Dominance, aggression and reproduction in primate societies. J. Theor. Biol. 60, 459–472 (1976).CAS 
    PubMed 
    Article 

    Google Scholar 
    94.Kaburu, S. S. K. et al. Rates of human-macaque interactions affect grooming behavior among urban-dwelling rhesus macaques (Macaca mulatta). Am. J. Phys. Anthropol. 168, 92–103 (2019).PubMed 
    Article 

    Google Scholar 
    95.Holekamp, K. E. & Smale, L. Dominance acquisition during mammalian social development: the “inheritance” of maternal rank. Am. Zool. 31, 306–317 (1991).Article 

    Google Scholar 
    96.Csardi, G. & Nepusz, T. The igraph software package for complex network research. Complex Syst. 1695, 1–9 (2006).
    Google Scholar 
    97.Baayen, R. H. Analyzing Linguistic Data: A Practical Introduction to Statistics Using R. (Cambridge University Press, 2008).98.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    99.Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).Article 

    Google Scholar 
    100.Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: Keep it maximal. J. Mem. Lang. 68, 255–278 (2013).Article 

    Google Scholar 
    101.Kulik, L., Amici, F., Langos, D. & Widdig, A. Sex differences in the development of aggressive behavior in rhesus macaques (Macaca mulatta). Int. J. Primatol. 36, 764–789 (2015).Article 

    Google Scholar 
    102.Schielzeth, H. & Forstmeier, W. Conclusions beyond support: Overconfident estimates in mixed models. Behav. Ecol. 20, 416–420 (2009).PubMed 
    Article 

    Google Scholar 
    103.McCullagh, P. & Nelder, J. A. Generalized Linear Models. (Chapman and Hall, 1989).104.Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84, 1144–1163 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    105.Farine, D. R. & Carter, G. G. Permutation tests for hypothesis testing with animal social data: problems and potential solutions. Preprint at https://doi.org/10.1101/2020.08.02.232710 (2021).106.Weiss, M. N. et al. Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models. Methods Ecol. Evol. 00, 1–11 (2020).
    Google Scholar 
    107.Fox, J. & Weisberg, S. An R Companion to Applied Regression. (Sage Publications, 2011).108.Field, A. Discovering Statistics Using IBM SPSS Statistics. (Sage Publications, 2013).109.Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists. (Cambridge University Press, 2002). More

  • in

    Multiple DNA marker-assisted diversity analysis of Indian mango (Mangifera indica L.) populations

    1.Purseglove, J. W. Mangoes west of India. Acta Hortic. 24, 107–174 (1972).
    Google Scholar 
    2.Mukherjee, S. K. Origin, distribution and phylogenetic affinities of the species of Mangifera indica L. Bot. J. Linn. Soc. 55, 65–83 (1953).Article 

    Google Scholar 
    3.Kostermans, A. J. G. H. & Bompard, J. M. The Mangoes: Their Botany, Nomenclature (Horticulture and Utilization. IBPGR Academic Press, 1993).
    Google Scholar 
    4.Ravishankar, K. V., Lalitha, A., Anand, L. & Dinesh, M. R. Assessment of genetic relatedness among mango cultivars of India using RAPD markers. J. Hortic. Sci. Biotech. 75, 198–201 (2000).CAS 
    Article 

    Google Scholar 
    5.Karihaloo, J. L., Dwivedi, Y. K., Archak, S. & Gaikwad, A. B. Analysis of genetic diversity of Indian mango cultivars using RAPD markers. J. Hortic. Sci. Biotech. 78, 285–289 (2003).CAS 
    Article 

    Google Scholar 
    6.APEDA, The Agricultural and Processed Food Products Export Development Authority http://apeda.gov.in/apedawebsite/sixheadproduct/FFV.htm (2017).7.National Horticultural Board, Ministry of Agriculture and Farmers Welfare Government of India 85, Institutional Area, Sector-18, Gurugram 122015 (Haryana), India http://www.nhb.gov.in (2016-17).8.Jena, R.C. DNA fingerprinting of some promising Indian genotypes and hybrids of mango (Mangifera indica L.). PhD Thesis (pp 1–422). Utkal University, India (2019).9.Yadav, I. S. & Rajan, S. Genetic resources of mango. Adv. Hortic. 1, 77–93 (1993).
    Google Scholar 
    10.Zhang, J. et al. Potential of start codon targeted (SCoT) markers to estimate genetic diversity and relationships among Chinese Elymus sibiricus accessions. Molecules 20, 5987–6001 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Harisaranraj, R., Prasitha, R., Saravana Babu, S. & Suresh, K. Analysis of inter-species relationships of Ocimum species using RAPD markers. Ethnobotanical Leaflets. 12, 609–613 (2008).
    Google Scholar 
    12.Liu, H. et al. Genetic diversity and population structure of the endangered plant Salix taishanensis based on CDDP markers. Glob Ecol. Conserv. 24, (2020).13.Mahar, K. S. et al. Estimation of genetic variability and population structure in Sapindus trifoliatus L., using DNA fingerprinting methods. Trees 27, 85–96 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    14.Kalpana, D. et al. Assessment of genetic diversity among varieties of mulberry using RAPD and ISSR fingerprinting. Sci. Hortic. 134, 79–87 (2012).CAS 
    Article 

    Google Scholar 
    15.Medhi, K. et al. High gene flow and genetic diversity in three economically important Zanthoxylum Spp. of Upper Brahmaputra Valley Zone of NE India using molecular markers. Meta Gene. 2, 706–721 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Wunsch, A. & Hormaza, J. I. Cultivar identification and genetic fingerprinting of temperate fruit tree species using DNA markers. Euphytica 125, 59–67 (2002).Article 

    Google Scholar 
    17.Flint-Garcia, S. A. et al. Maize association population: a high-resolution platform for quantitative trait locus dissection. Plant J. 44, 1054–1064 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Breton, C., Pinatel, C., Medail, F., Bonhomme, F. & Berville, A. Comparison between classical and Bayesian methods to investigate the history of olive cultivars using SSR-polymorphisms. Plant Sci. 175, 524–532 (2008).CAS 
    Article 

    Google Scholar 
    19.Pillon, Y., Qamaruz-Zaman, F., Fay, M. F., Hendoux, F. & Piquot, Y. Genetic diversity and ecological differentiation in the endangered fen orchid (Liparis loeselii). Conserv. Genet. 8, 177–184 (2007).Article 

    Google Scholar 
    20.Mahar, K. S., Rana, T. S., Ranade, S. A. & Meena, B. Genetic variability and population structure in Sapindus emarginatus Vahl from India. Gene 485, 32–39 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    21.Izawa, T., Kawahara, T. & Takahashi, H. Genetic diversity of an endangered plant, Cypripedium macranthosvar. rebunense (Orchidaceae): Background genetic research for future conservation. Conserv. Genet. 8, 1369–1376 (2007).Article 

    Google Scholar 
    22.Neel, M. C. & Ellstrand, N. C. Conservation of genetic diversity in the endangered plant Eriogonum ovalifolium var. vineum (Polygonaceae). Conserv. Genet. 4, 337–352 (2003).CAS 
    Article 

    Google Scholar 
    23.George, S., Sharma, J. & Yadon, V. L. Genetic diversity of the endangered and narrow endemic Piperia yadonii (Orchidaceae) assessed with ISSR polymorphisms. Am. J. Bot. 96, 2022–2030 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Marsjan, P. & Oldenbroek, J.K. Molecular markers, a tool for exploring genetic diversity. The State of the World’s Animal Genetic Resources for Food and Agriculture, (pp. 359–379). FAO Research report, Rome (2007).25.Kumar, P., Gupta, V. K., Misra, A. K., Modi, D. R. & Pandey, B. K. Potential of molecular markers in plant biotechnology. Plant Omics. 2, 141–162 (2009).CAS 

    Google Scholar 
    26.Agarwal, M., Shrivastava, N. & Padh, H. Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Rep. 27, 617–631 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Li, M., Zhao, Z. & Miao, X. J. Genetic variability of wild apricot (Prunus Armeniaca L.) populations in the Ili Valley as revealed by ISSR markers. Genet. Resour. Crop Evol. 60, 2293–2302 (2013).CAS 
    Article 

    Google Scholar 
    28.Abdin, M. Z. et al. Population structure and genetic diversity in bottle gourd [Lagenaria siceraria (Mol.) Standl.] germplasm from India assessed by ISSR marker. Plant Syst. Evol. 300, 767–773 (2014).Article 

    Google Scholar 
    29.Fazeli, S., Sheidai, M., Farahani, F. & Noormohammadi, Z. Looking for genetic diversity in Iranian apple cultivars (Malus × domestica Borkh.). J Sci. 27, 205–221 (2016).
    Google Scholar 
    30.Qian, X., Wang, C. & Tian, M. Genetic diversity and population differentiation of Calanthe tsoongiana, a rare and endemic orchid in China. Int J Mol Sci. 14, 20399–20413 (2013).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    31.Singh, N. et al. Comparison of SSR and SNP markers in estimation of genetic diversity and population structure of Indian rice varieties. PLoS ONE 8(12), e84136. https://doi.org/10.1371/journal.pone.0084136 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Jena, R. C., Agarwal, K., Ghosh, T. S. & Chand, P. K. Population structuring of selected mungbean landraces of the Odisha State of India via DNA marker-based genetic diversity analysis. Agric. Gene. 3, 67–86 (2017).Article 

    Google Scholar 
    33.Dias, A. et al. Portuguese Pinus nigra JF Arnold populations: genetic diversity, structure and relationships inferred by SSR markers. Ann. For. Sci. 77, 1–15 (2020).
    Google Scholar 
    34.Wu, Q., Zang, F., Ma, Y., Zheng, Y. & Zang, D. Analysis of genetic diversity and population structure in endangered Populus wulianensis based on 18 newly developed EST-SSR markers. Glob. Ecol. Conserv. 24, e01329 (2020).Article 

    Google Scholar 
    35.Surapaneni, M. et al. Population structure and genetic analysis of different utility types of mango (Mangifera indica L.) germplasm of Andhra Pradesh state of India using microsatellite markers. Plant Syst. Evol. 299, 1215–1229 (2013).CAS 
    Article 

    Google Scholar 
    36.Yilmaz, K. U., Paydas-Kargi, S., Dogan, Y. & Kafkas, S. Genetic diversity analysis based on ISSR, RAPD and SSR among Turkish apricot germplasms in Iran Caucasian eco-geographical group. Sci. Hortic. 138, 138–143 (2012).CAS 
    Article 

    Google Scholar 
    37.Patel, H. K., Fougat, R. S., Kumar, S., Mistry, J. G. & Kumar, M. Detection of genetic variation in Ocimum species using RAPD and ISSR markers. 3. Biotech 5, 697–707 (2015).
    Google Scholar 
    38.Desai, P. et al. Comparative assessment of genetic diversity among Indian bamboo genotypes using RAPD and ISSR markers. Mol. Biol. Rep. 42, 1265–1273 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Luo, C. et al. Genetic diversity of mango cultivars estimated using SCoT and ISSR markers. Biochem. Syst. Ecol. 39, 676–684 (2011).CAS 
    Article 

    Google Scholar 
    40.Gajera, H. P., Tomar, R. S., Patel, S. V., Viradia, R. R. & Golakiya, B. A. Comparison of RAPD and ISSR markers for genetic diversity analysis among different endangered Mangifera indica genotypes of Indian Gir forest region. J. Plant Biochem. Biotech. 20, 217–223 (2011).Article 

    Google Scholar 
    41.Hamrick, J. L. & Godt, M. J. W. Conservation genetics of endemic plant species. In Avise, J. C., & J. L. Hamrick (Eds.), Conservation genetics: case histories from nature. (pp. 281–30). Chapman and Hall, New York (1996).42.Wang Z, Kang M, Liu H, Gao J, Zhang Z, Li Y, Wu R, Pang X (2014). High-level genetic diversity and complex population structure of Siberian apricot (Prunus sibirica L.) in China as revealed by nuclear SSR markers. PLOS ONE 9:e87381PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    43.Xie, W. G., Zhang, X. Q., Ma, X., Huang, L. K. & Zeng, B. Genetic variation of Dactylis glomerata germplasm from Southwest China detected by SSR markers. Acta Pratacult. 18, 138–146 (2009).
    Google Scholar 
    44.Yan, X. B., Guo, Y. X., Zhou, H. & Wang, K. Analysis of geographical conditions affected on genetic variation and relationship among populations of Elymus. J. Plant Res. Environ. 15, 17–24 (2006).
    Google Scholar 
    45.Hamrick, J. L., Godt, M. J. W. & Sherman-Broyles, S. L. Factors influencing levels of genetic diversity in plant species. New For. 6, 95–124 (1992).Article 

    Google Scholar 
    46.Li, M., Zhao, Z. & Miao, X. Genetic diversity and relationships of apricot cultivars in North China revealed by ISSR and SRAP markers. Sci. Hortic. 173, 20–28 (2014).Article 

    Google Scholar 
    47.Kubik, C., Honig, J., Meyer, W. A. & Stacy, A. B. Genetic diversity of creeping bent-grass cultivars using SSR markers. Int. Turfgrass Soc. Res. J. 11, 533–547 (2009).
    Google Scholar 
    48.Gupta, P. K. & Roy, J. K. Molecular markers in crop improvement: Present status and future needs in India. Plant Cell Tiss. Org. 70, 229–234 (2002).Article 

    Google Scholar 
    49.Sivaprakash, K. R., Prasanth, S. R., Mohanty, B. P. & Parida, A. Genetic diversity of black gram landraces as evaluated by AFLP markers. Curr. Sci. 86, 1411–1415 (2004).
    Google Scholar 
    50.Noormohammadi, Z. et al. Genetic Variation among Iranian Pomegranates (Punica granatum L.) using RAPD, ISSR and SSR Markers. Aust. J. Crop Sci. 6, 268–275 (2012).CAS 

    Google Scholar 
    51.Schaal, B. A., Hayworth, D. A., Olsen, K. M., Rauscher, J. T. & Smith, W. A. Phylogeographic studies in plants: problems and prospects. Mol. Ecol. 7, 464–474 (1998).Article 

    Google Scholar 
    52.Zong, M. et al. Genetic diversity in geographic differentiation in the threatened species Dysosma pleiantha in China as revealed by ISSR analysis. Biochem. Genet. 46, 180–196 (2008).MathSciNet 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Wright, S. Evolution and the Genetics of Population (University of Chicago Press, 1978).
    Google Scholar 
    54.Slatin, M. Gene flow and geographic structure of natural populations. Science 236, 787–792 (1987).ADS 
    Article 

    Google Scholar 
    55.Kumar, A., Mishra, P., Singh, S. C. & Sundaresan, V. Efficiency of ISSR and RAPD markers in genetic divergence analysis and conservation management of Justicia adhatoda L., a medicinal plant. Plant Syst. Evol. 300, 1409–1420 (2014).Article 

    Google Scholar 
    56.Slatkin, M. & Barton, N. H. A comparison of three indirect methods for estimating the average level of gene flow. Evolution 43, 1349–1368 (1989).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Kouam, E. B., Pasquet, R. S., Elteraifi, I. & Muluvi, G. M. Genetic diversity and population structure of Vigna unguiculata ssp. unguiculata var. spontanea in Sudan. J. Res. Biol. 8, 643–652 (2011).
    Google Scholar 
    58.Xing, C., Tian, Y. & Meng, F. Evaluation of genetic diversity in Amygdalus mira (Koehne) Ricker using SSR and ISSR markers. Plant Syst. Evol. 301, 1055–1064 (2015).Article 

    Google Scholar 
    59.Ikegami, H., Nogata, H., Hirashima, K., Awamura, M. & Nakahara, T. Analysis of genetic diversity among European and Asian fig varieties (Ficus carica L.) using ISSR, RAPD, and SSR markers. Genet. Resour. Crop Evol. 56, 201–209 (2009).CAS 
    Article 

    Google Scholar 
    60.Takrouni, M. M. & Boussaid, M. Genetic diversity and population’s structure in Tunisian strawberry tree (Arbutus undo L.). Sci. Hortic. 126, 330–337 (2010).Article 

    Google Scholar 
    61.Arya, L., Narayanan, R. K., Verma, M., Singh, A. K. & Gupta, V. Genetic diversity and population structure analyses of Morinda tomentosa Heyne, with neutral and gene based markers. Genet. Resour. Crop Evol. 61, 1469–1479 (2014).CAS 
    Article 

    Google Scholar 
    62.Hamrick, J. L., Godt, M. J. W., Murawski, D. A., & Loveless, M. D. Correlations between species traits and allozyme diversity: Implications for conservation biology. In Falk, D.A.S., & K. E. Holsinger (Eds.), Genetics and conservation of rare plants. (pp. 75–86), Oxford University Press, Oxford (1991).63.Loveless, M. D. & Hamrick, J. L. Ecological determinants of genetic structure in plant populations. Annu. Rev. Ecol. Evol. Syst. 15, 65–96 (1984).Article 

    Google Scholar 
    64.Schoen, D. J. & Brown, A. H. D. Intraspecific variation in population gene diversity and effective population size correlates with mating systems in plants. Proc. Natl. Acad. Sci. USA 88, 4494–4497 (1991).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    65.Yan, J. J., Bai, S. Q., Zhang, X. Q. & Chang, D. Genetic diversity of native Elymus sibiricus populations in the Southeastern Margin of Qinghai-Tibetan Plateau as detected by SRAP and SSR marker. Acta Pratacult. Sin. 19, 122–134 (2010).
    Google Scholar 
    66.Aros, D., Meneses, C. & Infante, R. Genetic diversity of wild species and cultivated varieties of alstroemeria estimated through morphological descriptors and RAPD markers. Sci. Hortic. 108, 86–90 (2006).CAS 
    Article 

    Google Scholar 
    67.Souframanien, J. & Gopalakrishna, T. A comparative analysis of genetic diversity in black gram genotypes using RAPD and ISSR markers. Theor. Appl. Genet. 109, 1687–1693 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Gorji, A. M., Poczai, P., Polgar, Z. & Taller, J. Efficiency of arbitrarily amplified dominant markers (SCoT, ISSR and RAPD) for diagnostic fingerprinting in tetraploid potato. Am. J. Potato Res. 88, 226–237 (2011).Article 

    Google Scholar 
    69.Saxena, S. et al. Analysis of genetic diversity among papaya cultivars using single primer amplification reaction (SPAR) methods. J. Hortic. Sci. Biotech. 80, 291–296 (2005).CAS 
    Article 

    Google Scholar 
    70.Murty, S. G. et al. Comparison of RAPD, ISSR and DAMD markers for genetic diversity assessment between accessions of Jatropha curcas L., and its related species. J. Agric. Sci Tech. 15, 1007–1022 (2013).CAS 

    Google Scholar 
    71.Ferrao, L. F. V. et al. Comparative study of different molecular markers for classifying and establishing genetic relationships in Coffea canephora. Plant. Syst. Evol. 299, 225–238 (2013).CAS 
    Article 

    Google Scholar 
    72.Doyle, J. J. & Doyle, J. L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 19, 11–15 (1987).
    Google Scholar 
    73.Doyle, J. J. & Doyle, J. L. Isolation of plant DNA from fresh tissue. Focus 12, 13–15 (1990).
    Google Scholar 
    74.Sambrook, J., Fritsch, E. F. & Maniatis, T., Agarose gel electrophoresis of DNA and pulse field gel electrophoresis. In: Molecular Cloning: a Laboratory Manual, 3rd Edn. Cold Springer Harbor Laboratory Press, (pp. 5.1–5.86). New York, USA (1989).75.Zhou, Z., Bebeli, P. J., Somers, D. J. & Gustafson, J. P. Direct amplification of minisatellite-region DNA with VNTR core sequences in the genus Oryza. Theor. Appl. Genet. 95, 942–949 (1997).CAS 
    Article 

    Google Scholar 
    76.Winberg, B. C., Shori, Z., Dallas, J. F., Mclntyre, C. L. & Gustafson, J. P. Characterization of minisatellite sequences from Oryza sativa. Genome 36, 978–983 (1993).CAS 
    PubMed 
    Article 

    Google Scholar 
    77.Kang, H. W., Park, D. S., Go, S. J. & Eun, M. Y. Fingerprinting of diverse genomes using PCR with universal rice primers generated from repetitive sequence of Korean weedy rice. Mol. Cell. 13, 281–287 (2002).CAS 

    Google Scholar 
    78.Jeffreys, A. J., Wilson, V. & Thein, S. L. Hypervariable minisatellite regions in human DNA. Nature 314, 67–72 (1985).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    79.Nakamura, Y. et al. Variable number of tandem repeats (VNTR) markers for human gene mapping. Science 235, 1616–1622 (1987).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    80.Anderson, T. H. & Nilsson-Tillgren, T. A fungal minisatellite. Nature 386, 771 (1997).ADS 
    Article 

    Google Scholar 
    81.Collard, B. C. Y. & Mackill, D. J. Start Codon Targeted (SCoT) polymorphism: a simple novel DNA marker technique for generating gene-targeted markers in plants. Plant. Mol. Biol. Rep. 27, 86–93 (2009).CAS 
    Article 

    Google Scholar 
    82.Luo, C., He, X. H., Chen, H., Ou, S. J. & Gao, M. P. Analysis of diversity and relationships among mango cultivars using start codon targeted (SCoT) markers. Biochem. Syst. Ecol. 38, 1176–1184 (2010).CAS 
    Article 

    Google Scholar 
    83.Singh, A. K. et al. CAAT box-derived polymorphism (CBDP): A novel promoter-targeted molecular marker for plants. J. Plant Biochem. Biotech. 23, 175–183 (2013).Article 
    CAS 

    Google Scholar 
    84.Schnell, R. J., Olano, C. T., Quintanilla, W. E. & Meerow, A. W. Isolation and characterization of 15 microsatellite loci from mango (Mangifera indica L.) and cross-species amplification in closely related taxa. Mol. Ecol. Notes. 5, 625–627 (2005).CAS 
    Article 

    Google Scholar 
    85.Viruel, M. A., Escribano, P., Barbieri, M., Ferri, M. & Hormaza, J. I. Fingerprint, embryo type, and geographic differentiation in mango (Mangifera indica L., Anacardiaceae) with microsatellites. Mol. Breed. 15, 383–393 (2005).CAS 
    Article 

    Google Scholar 
    86.Ukoskit, K. Development of microsatellite markers in mango (Mangifera indica L.) using 5’ anchored PCR. Thammasat. Int. J. Sci. Tech. 12, 1–7 (2007).
    Google Scholar 
    87.Ravishankar, K. V., Mani, B. H. R., Anand, L. & Dinesh, M. R. Development of new microsatellite markers from mango (Mangifera indica) and cross-species amplification. Am. J. Bot. 98, 96–99 (2011).Article 

    Google Scholar 
    88.Yeh, F.C., Yang, R.C. & Boyle, T., POPGENE Version 1.32: Microsoft Window-Based Freeware for Population Genetics Analysis, (p. 12). University of Alberta, Edmonton (1999).89.Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28, 2537–2539 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    90.Jaccard, P. Nouvellesrecherchessur la distribution florale. Bull. Soc. vaudoise sci. nat. 44, 223–270 (1908).
    Google Scholar 
    91.Rohlf, F.J. NTSYS pc numerical taxonomy and multivariate system, Version 2.1.Exeter Publ Ltd, Setauket, New York (1993).92.Sneath, P. H. A. & Sokal, R. R. Numerical taxonomy (Freeman Press, 1973).MATH 

    Google Scholar 
    93.Nei, M. Genetic distance between populations. Am. Nat. 106, 283–392 (1972).Article 

    Google Scholar 
    94.Yap, V., Nelson, R. J. WinBoot: A program for performing bootstrap analysis of binary data to determine the confidence limits of UPGMA-based dendrograms. IRRI, Philippines (1996). More

  • in

    DOM degradation by light and microbes along the Yukon River-coastal ocean continuum

    1.Holmes, R. M. et al. Seasonal and annual fluxes of nutrients and organic matter from large rivers to the arctic ocean and surrounding seas. Estuaries Coasts 35(2), 369–382 (2011).Article 
    CAS 

    Google Scholar 
    2.Peterson, B.J., Holmes, R.M., McClelland, J.W., Vörösmarty, C.J., Lammers, R.B., Shiklomanov, A. et al. Increasing river discharge to the Arctic Ocean. Science 298, 2171-2173 (2002).
    3.McClelland, J.W., Déry, S.J., Peterson, B.J., Holmes, R.M., Wood, E.F. A pan-arctic evaluation of changes in river discharge during the latter half of the 20th century. Geophys. Res. Lett. 33(6), (2006).4.Spencer, R. G. M. et al. Detecting the signature of permafrost thaw in Arctic rivers. Geophys. Res. Lett. 42(8), 2830–2835 (2015).ADS 
    Article 

    Google Scholar 
    5.O’Donnell, J. A. et al. Dissolved organic matter composition of Arctic rivers: Linking permafrost and parent material to riverine carbon. Glob. Biogeochem. Cycles 30(12), 1811–1826 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    6.Kirchman, D. L., Malmstrom, R. R. & Cottrell, M. T. Control of bacterial growth by temperature and organic matter in the Western Arctic. Deep Sea Res. Part II 52(24–26), 3386–3395 (2005).ADS 
    Article 

    Google Scholar 
    7.Mann, P.J., Davydova, A., Zimov, N., Spencer, R.G.M., Davydov, S., Bulygina, E. et al. Controls on the composition and lability of dissolved organic matter in Siberia’s Kolyma River basin. J. Geophys. Res. Biogeosci. 117(G1), (2012).8.Crump, B. C., Kling, G. W., Bahr, M. & Hobbie, J. E. Bacterioplankton community shifts in an arctic lake correlate with seasonal changes in organic matter source. Appl. Environ. Microbiol. 69(4), 2253–2268 (2003).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Docherty, K. M., Young, K. C., Maurice, P. A. & Bridgham, S. D. Dissolved organic matter concentration and quality influences upon structure and function of freshwater microbial communities. Microb. Ecol. 52(3), 378–388 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    10.Elifantz, H., Malmstrom, R. R., Cottrell, M. T. & Kirchman, D. L. Assimilation of polysaccharides and glucose by major bacterial groups in the Delaware Estuary. Appl. Environ. Microbiol. 71(12), 7799–7805 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Nalven, S. G. et al. Experimental metatranscriptomics reveals the costs and benefits of dissolved organic matter photo-alteration for freshwater microbes. Environ. Microbiol. 22(8), 3505–3521 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Ward, C. P. & Cory, R. M. Complete and partial photo-oxidation of dissolved organic matter draining permafrost soils. Environ. Sci. Technol. 50(7), 3545–3553 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    13.Ward, C. P., Nalven, S. G., Crump, B. C., Kling, G. W. & Cory, R. M. Photochemical alteration of organic carbon draining permafrost soils shifts microbial metabolic pathways and stimulates respiration. Nat. Commun. 8(1), 772 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    14.Kaiser, K., Canedo-Oropeza, M., McMahon, R. & Amon, R. M. W. Origins and transformations of dissolved organic matter in large Arctic rivers. Sci. Rep. 7(1), 13064 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    15.Soares, A. R. A., Lapierre, J. F., Selvam, B. P., Lindstrom, G. & Berggren, M. Controls on dissolved organic carbon bioreactivity in river systems. Sci. Rep. 9(1), 14897 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    16.Pegau, W.S. Inherent optical properties of the central Arctic surface waters. J. Geophys. Res. Oceans, 107(C10), SHE-16 (2002).17.Kim, G. E., Pradal, M.-A. & Gnanadesikan, A. Increased surface ocean heating by colored detrital matter (CDM) linked to greater Northern Hemisphere ice formation in the GFDL CM2Mc ESM. J. Clim. 29(24), 9063–9076 (2016).ADS 
    Article 

    Google Scholar 
    18.Laglera, L. M. et al. First quantification of the controlling role of Humic substances in the transport of iron across the surface of the Arctic Ocean. Environ. Sci. Technol. 53(22), 13136–13145 (2019).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Charette, M.A., Kipp, L.E., Jensen, L.T., Dabrowski, J.S., Whitmore, L.M., Fitzsimmons, J.N. et al. The transpolar drift as a source of riverine and shelf‐derived trace elements to the Central Arctic Ocean. J. Geophys. Res. Oceans 125(5), (2020). https://doi.org/10.1029/2019JC015920.20.Amon, R. M. W. et al. Dissolved organic matter sources in large Arctic rivers. Geochim. Cosmochim. Acta 94, 217–237 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    21.Spencer, R.G.M., Aiken, G.R., Wickland, K.P., Striegl, R.G., Hernes, P.J. Seasonal and spatial variability in dissolved organic matter quantity and composition from the Yukon River basin, Alaska. Glob. Biogeochem. Cycles 22(4), (2008).22.Mann, P.J., Spencer, R.G.M., Hernes, P.J., Six, J., Aiken, G.R., Tank, S.E., et al. Pan-Arctic trends in terrestrial dissolved organic matter from optical measurements. Front. Earth Sci. 4(25), (2016).23.Hernes, P. J. & Benner, R. Terrigenous organic matter sources and reactivity in the North Atlantic Ocean and a comparison to the Arctic and Pacific oceans. Mar. Chem. 100(1–2), 66–79 (2006).CAS 
    Article 

    Google Scholar 
    24.Catalá, T.S., Reche, I., Fuentes-Lema, A., Romera-Castillo, C., Nieto-Cid, M., Ortega-Retuerta, E., et al. Turnover time of fluorescent dissolved organic matter in the dark global ocean. Nature communications 6(1), 1–9 (2015).25.Colatriano, D., Tran, P.Q., Guéguen, C., Williams, W.J., Lovejoy, C., Walsh, D.A. Genomic evidence for the degradation of terrestrial organic matter by pelagic Arctic Ocean Chloroflexi bacteria. Commun. Biol. 1(1), 1–9 (2018).26.Müller, O., Seuthe, L., Bratbak, G., Paulsen, M.L. Bacterial response to permafrost derived organic matter input in an Arctic Fjord. Front. Mar. Sci. 5(263), (2018).27.Kujawinski, E.B., Longnecker, K., Barott, K.L., Weber, R.J.M., Kido Soule, M.C. Microbial community structure affects marine dissolved organic matter composition. Front. Mar. Sci. 3(45), (2016).28.Avila, M. P. et al. Linking shifts in bacterial community with changes in dissolved organic matter pool in a tropical lake. Sci. Total Environ. 672, 990–1003 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    29.Elifantz, H., Dittel, A. I., Cottrell, M. T. & Kirchman, D. L. Dissolved organic matter assimilation by heterotrophic bacterial groups in the western Arctic Ocean. Aquat. Microb. Ecol. 50, 39–49 (2007).Article 

    Google Scholar 
    30.Lee, J. et al. Latitudinal distributions and controls of bacterial community composition during the summer of 2017 in Western Arctic Surface Waters (from the Bering Strait to the Chukchi Borderland). Sci. Rep. 9(1), 16822 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    31.Fortunato, C. S. & Crump, B. C. Bacterioplankton community variation across river to ocean environmental gradients. Microb. Ecol. 62(2), 374–382 (2011).PubMed 
    Article 

    Google Scholar 
    32.Balmonte, J. P. et al. Sharp contrasts between freshwater and marine microbial enzymatic capabilities, community composition, and DOM pools in a NE Greenland fjord. Limnol. Oceanogr. 65(1), 77–95 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    33.Sipler, R. E. et al. Microbial community response to terrestrially derived dissolved organic matter in the Coastal Arctic. Front. Microbiol. 8, 1018 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Scully, N. M., Cooper, W. J. & Tranvik, L. J. Photochemical effects on microbial activity in natural waters: the interaction of reactive oxygen species and dissolved organic matter. FEMS Microbiol. Ecol. 46(3), 353–357 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Bélanger, S., Xie, H., Krotkov, N., Larouche, P., Vincent, W.F., Babin, M. Photomineralization of terrigenous dissolved organic matter in Arctic coastal waters from 1979 to 2003: Interannual variability and implications of climate change. Glob. Biogeochem. Cycles 20(4), (2006).36.Timko, S.A., Maydanov, A., Pittelli, S.L., Conte, M.H., Cooper, W.J., Koch, B.P. et al. Depth-dependent photodegradation of marine dissolved organic matter. Front. Mar. Sci. 2(66) (2015).37.Pisani, O., Yamashita, Y. & Jaffe, R. Photo-dissolution of flocculent, detrital material in aquatic environments: contributions to the dissolved organic matter pool. Water Res. 45(13), 3836–3844 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Coble, P. G. Characterization of marine and terrestrial DOM in seawater using excitation-emission matrix spectroscopy. Mar. Chem. 51, 325–346 (1996).CAS 
    Article 

    Google Scholar 
    39.Kothawala, D. N. et al. Controls of dissolved organic matter quality: Evidence from a large-scale boreal lake survey. Glob. Chang Biol. 20(4), 1101–1114 (2014).ADS 
    PubMed 
    Article 

    Google Scholar 
    40.Paerl, R. W., Claudio, I. M., Shields, M. R., Bianchi, T. S. & Osburn, C. L. Dityrosine formation via reactive oxygen consumption yields increasingly recalcitrant humic-like fluorescent organic matter in the ocean. Limnol. Oceanogr. Lett. 5(5), 337–345 (2020).CAS 
    Article 

    Google Scholar 
    41.Spencer, R.G.M., Aiken, G.R., Butler, K.D., Dornblaser, M.M., Striegl, R.G., Hernes, P.J. Utilizing chromophoric dissolved organic matter measurements to derive export and reactivity of dissolved organic carbon exported to the Arctic Ocean: A case study of the Yukon River, Alaska. Geophys. Res. Lett. 36(6), (2009).42.Maie, N., Scully, N. M., Pisani, O. & Jaffe, R. Composition of a protein-like fluorophore of dissolved organic matter in coastal wetland and estuarine ecosystems. Water Res. 41(3), 563–570 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Hernes, P.J., Bergamaschi, B.A., Eckard, R.S., Spencer, R.G.M. Fluorescence-based proxies for lignin in freshwater dissolved organic matter. J. Geophys. Res. 114(G4) (2009).44.Murphy, K. R., Stedmon, C. A., Wenig, P. & Bro, R. OpenFluor—An online spectral library of auto-fluorescence by organic compounds in the environment. Anal. Methods 6(3), 658–661 (2014).CAS 
    Article 

    Google Scholar 
    45.Lanzalunga, O. & Bietti, M. Photo- and radiation chemical induced degradation of lignin model compounds. J. Photochem. Photobiol. B 56(2–3), 85–108 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Brym, A. et al. Optical and chemical characterization of base-extracted particulate organic matter in coastal marine environments. Mar. Chem. 162, 96–113 (2014).CAS 
    Article 

    Google Scholar 
    47.Tanentzap, A. J. et al. Chemical and microbial diversity covary in fresh water to influence ecosystem functioning. Proc. Natl. Acad. Sci. USA 116(49), 24689–24695 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Jørgensen, L., Stedmon, C. A., Granskog, M. A. & Middelboe, M. Tracing the long-term microbial production of recalcitrant fluorescent dissolved organic matter in seawater. Geophys. Res. Lett. 41(7), 2481–2488 (2014).ADS 
    Article 
    CAS 

    Google Scholar 
    49.McDonald, N., Achterberg, E.P., Carlson, C.A., Gledhill, M., Liu, S., Matheson-Barker, J.R. et al. The role of heterotrophic bacteria and archaea in the transformation of lignin in the open ocean. Front. Mar. Sci. 6(743), (2019).50.Zark, M. & Dittmar, T. Universal molecular structures in natural dissolved organic matter. Nat. Commun. 9(1), 3178 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    51.Harfmann, J. L. et al. Convergence of terrestrial dissolved organic matter composition and the role of microbial buffering in aquatic ecosystems. J. Geophys. Res. Biogeosci. 124(10), 3125–3142 (2019).CAS 
    Article 

    Google Scholar 
    52.Wünsch, U. J., Bro, R., Stedmon, C. A., Wenig, P. & Murphy, K. R. Emerging patterns in the global distribution of dissolved organic matter fluorescence. Anal. Methods 11(7), 888–893 (2019).Article 

    Google Scholar 
    53.Fitch, A., Orland, C., Willer, D., Emilson, E. J. S. & Tanentzap, A. J. Feasting on terrestrial organic matter: Dining in a dark lake changes microbial decomposition. Glob. Chang Biol. 24(11), 5110–5122 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Saw, J.H.W., Nunoura, T., Hirai, M., Takaki, Y., Parsons, R., Michelsen, M. et al. Pangenomics analysis reveals diversification of enzyme families and niche specialization in globally abundant SAR202 bacteria. mBio 11(1), (2020).55.Min, D. W. et al. Abiotic formation of humic-like substances through freezing-accelerated reaction of phenolic compounds and nitrite. Environ. Sci. Technol. 53(13), 7410–7418 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    56.Dagley, S. & Gibson, D. The bacterial degradation of catechol. Biochem. J. 95(2), 466–474 (1965).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    57.Kraepiel, A. M., Bellenger, J. P., Wichard, T. & Morel, F. M. Multiple roles of siderophores in free-living nitrogen-fixing bacteria. Biometals 22(4), 573–581 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    58.Stedmon, C. A. & Markager, S. Behaviour of the optical properties of coloured dissolved organic matter under conservative mixing. Estuar. Coast. Shelf Sci. 57(5–6), 973–979 (2003).ADS 
    CAS 
    Article 

    Google Scholar 
    59.Servais, P., Courties, C., Lebaron, P. & Troussellier, M. Coupling bacterial activity measurements with cell sorting by flow cytometry. Microb. Ecol. 38(2), 180–189 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Newton, R. J. & Shade, A. Lifestyles of rarity: Understanding heterotrophic strategies to inform the ecology of the microbial rare biosphere. Aquat. Microb. Ecol. 78(1), 51–63 (2016).Article 

    Google Scholar 
    61.Amado, A. M., Cotner, J. B., Cory, R. M., Edhlund, B. L. & McNeill, K. Disentangling the interactions between photochemical and bacterial degradation of dissolved organic matter: Amino acids play a central role. Microb. Ecol. 69(3), 554–566 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    62.Mestre, M., Borrull, E., Sala, M. & Gasol, J. M. Patterns of bacterial diversity in the marine planktonic particulate matter continuum. ISME J. 11(4), 999–1010 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Gundersen, K., Bratbak, G., Heldal, M. Factors influencing the loss of bacteria in preserved seawater samples. Marine ecology progress
    series 137, 305–310 (1996).64.Logozzo, L., Tzortziou, M., Neale, P., Clark, B. Photochemical and microbial degradation of chromophoric dissolved organic matter exported from tidal marshes. J. Geophys. Res. Biogeosci. 126, e2020JG005744. https://doi.org/10.1029/2020JG005744(2021).65.Tzortziou, M. et al. Tidal marshes as a source of optically and chemically distinctive colored dissolved organic matter in the Chesapeake Bay. Limnol. Oceanogr. 53(1), 148–159 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    66.Grunert B. bricegrunert/cdom: Version 1 (Version v1.0). 2020, December 23 (ed: Zenodo). https://doi.org/10.5281/zenodo.439109767.Green, S. A. & Blough, N. V. Optical absorption and fluorescence properties of chromophoric dissolved organic matter in natural waters. Limnol. Oceanogr. 39(8), 1903–1916 (1994).ADS 
    CAS 
    Article 

    Google Scholar 
    68.Weishaar, J. L. et al. Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon. Environ. Sci. Technol. 37(20), 4702–4708 (2003).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    69.Andersen, C. M. & Bro, R. Practical aspects of PARAFAC modeling of fluorescence excitation-emission data. J. Chemom. 17(4), 200–215 (2003).CAS 
    Article 

    Google Scholar 
    70.Bahram, M., Bro, R., Stedmon, C. & Afkhami, A. Handling of Rayleigh and Raman scatter for PARAFAC modeling of fluorescence data using interpolation. J. Chemom. 20(3–4), 99–105 (2006).CAS 
    Article 

    Google Scholar 
    71.Murphy, K.R., Stedmon, C.A., Graeber, D., Bro, R. Fluorescence spectroscopy and multi-way techniques. PARAFAC. Anal. Methods 5(23), 6557-6566 (2013). More

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    Denitrifying bacteria respond to and shape microscale gradients within particulate matrices

    Nitrate (Nar) and nitrite (Nir) reductase expression in PAO1 were quantified with fluorescently tagged promoter fusions (Supplementary Fig. 1), green for the NarK subunit (NarK-GFP) and red for the NirS subunit (NirS-dsRed)35. As the first two steps in the denitrification pathway, expression of these genes indicates a metabolic switch from aerobic respiration with O2 to anaerobic respiration via denitrification. These PAO1 reporter strains were grown embedded within 3 mm diameter14 agarose particle discs held within a custom-built gastight millifluidic device (Fig. 1; Supplementary Methods), permitting continual lateral nutrient supply of rich nutrient media (with O2 and NO3–) from the bulk media via diffusion while maintaining a constant boundary condition at the particle periphery (Fig. 1a, b). This created an analog of a particle replete with dissolved organic nutrients whereby the agarose acted as an inert polymeric matrix rather than as a carbon source. Over ~24 h of growth in Luria-Bertani Broth (LB) media supplemented with NO3–, PAO1 formed densely packed stationary microcolonies (Supplementary Video 1), similar to its growth morphology in model alginate beads15,36 and agar blocks34. Seeding bacterial cells within a 1% agarose matrix allows colony expansion due only to growth (passive movement) and prevents active aerotactic and chemotactic movement driven by flagellar motility37. A subset of agarose particles contained biocompatible O2 nanosensors that could faithfully report O2 conditions varying dynamically over the scale of minutes (Supplementary Fig. 2).Fig. 1: Denitrifiers create anoxia within particles in fully aerated fluid.Agarose disc particles seeded with P. aeruginosa PAO1 were incubated with media in glass devices that permit only lateral diffusion into particles. a Topview and b sideview illustrating nutrient diffusion into particles as occurred in c, a millifluidic device with directional flow of air-saturated fluid, or a ‘domino’ device held within sealed glass bottles. Scale bar ~1.5 cm. a inset, PAO1 cells grew as dense microcolonies. d Air saturation across four particles as determined from microscopic signal of fluorescent oxygen nanosensors. Despite aerobic bulk fluid surrounding the particles, cell growth created anoxia and its onset depended on PAO1 seeding density, ranging 104–106 cells mL−1 (70–7000 cells particle−1; the 105 mL−1 cell density was tested for duplicate particles). Gray shading indicates time range shown in e and asterisks coincide with noted timepoints in e. e The air saturation for radial profiles across a single particle (105 cells mL−1 seeding) during the cellular respiration-driven transition from air-saturated to anoxic conditions. Suboxia first developed in the core (x  > 1000 μm) then spread to the particle periphery (x 103 microcolonies within each particle (e.g., Fig. 2). Both Nar and Nir expression were skewed toward the particle edge for lower bulk NO3– concentrations (Fig. 2b, c, Supplementary Fig. 4), indicative of a low NO3– flux reaching microcolonies in the particle core. In contrast, homogenous Nar expression occurred across all radial distances with 4 mM bulk NO3– (Fig. 2b), which suggests NO3– was nonlimiting for expression throughout the particle at such high bulk NO3–. Meanwhile, reduced Nir expression across the particle at 4 mM NO3– compared with lower NO3– treatments (Fig. 2c) indicates PAO1 preferentially reduced NO3– over NO2– throughout the particle under such NO3–-replete conditions. Bulk NO3– also influenced microcolony size distributions (Fig. 2b, c), with larger microcolonies manifesting near the periphery closer to the NO3– supply from the bulk fluid. Higher bulk NO3– stimulated greater microcolony growth in the particle core, i.e., at the center, the mean microcolony radius r was 6.4 ± 0.1 µm (sd) at 40 µM bulk NO3– but 12.7 ± 0.1 µm (sd) at 4 mM. Notably, under 4 mM bulk NO3–, size was skewed toward the particle periphery even while Nar expression was not, suggesting maximum cell-specific Nar expression rates occurred across all radial locations despite biomass production in the particle core remaining NO3– limited.Fig. 2: Denitrification gene expression and microcolony size within particles in fully anoxic fluid.a Example images showing PAO1 microcolonies expressing NarK-GFP (nitrate reductase) after 40 h of growth in bulk anoxic media amended with 3 nitrate concentrations. Scale bar = 700 μm. b Mean relative expression of NarK-GFP for microcolonies, and mean radii of those microcolonies, shown in relationship to the radial distance to the nearest particle edge. The mean NarK-GFP expression for microcolonies in each particle (3–4 replicates) is indicated by a thin green line and the mean for all particles by a thick line. Similarly, an exponential model fit of microcolony radii for each particle is indicated by a thin black line and the mean for all particles by a thick line inset, Results for one example particle in a, illustrating the data for n microcolonies relative to the respective fits for those data. c Same as b but for reporter strain NirS-dsRed (nitrite reductase) expression in separate particles incubated in parallel.Full size imageNot only did particle denitrification readily occur in anoxic bulk fluid as expected, it was also prevalent among particles in oxygenated bulk fluid. Moreover, the spatiotemporal expression of Nar and Nir coincided with the development and microscale distribution of suboxic conditions. In these experiments, agarose particles were embedded with either NarK-GFP or NirS-dsRed PAO1 (106 cells mL−1) as previously, then incubated in partially oxygenated LB media (50% air saturation) containing NO3– (40 µM). A subset of particles was co-embedded with oxygen nanosensors to enable imaging of the O2 landscape. Minimal Nar and Nir expression were detected for the first 14 h of incubation. Strikingly, Nar and Nir activation then occurred as a wave that traced the initiation and expansion of suboxia (Fig. 3a–c). At the earliest stage of the wave (following 16–18 h of growth), expression increased substantially in the vicinity of the particle core (Fig. 3d), crafting a transition zone of only 390 ± 50 µm (sd) width that differentiated low- from high-expression microcolonies (Fig. 3e, Supplementary Fig. 5, Supplementary Fig. 6). This upregulation of expression coincided spatially with the development of anoxia in the core (Fig. 4, Supplementary Fig. 7; (x) > 1000 µm). During the next stage of the wave (t = 20–22 h), expression near the core elevated further while also initiating farther afield, widening the transition zone and reflecting expansion of suboxia toward the particle periphery. As the wave progressed (t = 24–28 h), expression diminished near the core, and consequently, the transition zone width contracted (200 ± 15 µm (sd)) even while its outermost edge neared to within 200 µm of the particle edge, where O2 conditions had become anoxic (Fig. 4). Finally, in the final stage of the wave (exemplified at t = 40 h), high expression was confined to a fine band distantly from the core, yet not directly at the edge, as evidenced by a very narrow transition zone (57 ± 13 µm (sd) width). Notably, at the last stage, O2 throughout the particle was elevated above the minimal observed levels, in contrast to previous stages (t = 24–28 h; Fig. 4, Supplementary Fig. 7). This may reflect anoxic acclimation by PAO1 to preferentially perform denitrification over oxidative respiration thereby permitting O2 to diffuse more readily through the particle matrix.Fig. 3: Radial migration of denitrification expression within particles in partially aerated fluid.Particles seeded with cells were incubated in LB media saturated with 50% air and supplemented with 40 μM NO3–, then stopped at various timepoints. a Example images showing PAO1 microcolonies expressing NarK-GFP (nitrate reductase). Scale bar = 700 μm and applies to all images. Separate particles with the reporter strain for NirS-dsRed (nitrite reductase) were incubated in parallel. b Relative expression of NarK-GFP and c NirS-dsRed in particles. All microcolonies from 3–4 replicate particles per timepoint are represented. Nar expression initiates at the particle core while Nir expression initiates just proximal to it. For both Nar and Nir, maximal expression migrates outward creating a wave over subsequent timepoints. d Relative microcolony expression (as in b and c) shown as a microcolony’s radial location within the particle. An expression intensity fit was calculated as mean fits of each strain at each timepoint (Supplementary Note). Here the maximum fit value for each reporter strain at t = 24 h was set equal to 1, and shown are the expression values for all microcolonies relative to 1. e The radial location and range of the transition zone for each timepoint, approximated as the sloped region closest to the particle edge in the expression profile for NarK-GFP (green) and NirS-dsRed (red) (see also Supplementary Fig. 6). The midpoint of the slope (white diamonds) and the inflection point of the slope (circles) are indicated. NarK-GFP shows significantly higher expression between timepoints for midpoints (one-way ANOVA; F 1,35 = 27.9, p = 4.0 × 10−11) and for inflection points (one-way ANOVA; F 1,35 = 17.2, p = 9.5 × 10−9). NirS-dsRed also showed significantly higher expression between timepoints for midpoints (one-way ANOVA; F 1,35 = 14.9, p = 4.5 × 10−8) and for inflection points (one-way ANOVA; F 1,35 = 21.0, p = 1.0 × 10−9).Full size imageFig. 4: Evolution of anoxia within particles in partially aerated fluid.Two-dimensional profiles of air saturation were generated from particles co-seeded with the NarK-GFP reporter strain (nitrate reductase) and oxygen nanosensors. Scale bar = 700 μm. Separate analogous particles with the NirS-dsRed reporter strain (nitrite reductase) were incubated in parallel (Supplementary Fig. 7). For both, suboxic conditions develop in the particle core and then migrate outward toward the particle periphery over time.Full size imageThroughout the evolution of particle suboxia, Nar and Nir activity corresponded with the estimated spatiotemporal availability of O2 and NO3– in particles. Curve fits for microcolony fluorescence signal data were generated by assuming that O2 and NO3– concentrations at each radial location were the only controlling factors on expression, wherein O2 inhibits expression exponentially and NO3– has a directly proportional relationship, i.e., (Epropto {e}^{-k[{{mathrm{O}}}_{2}]}times [{mathrm{N}}{mathrm{{O}}}_{3}^{-}]). Approximating the distributions of O2 and NO3– in the particle as simple logistic functions, denitrification gene expression, E, is governed by the following relationship:$$E=alpha times {e}^{-beta left[2{[{{mathrm{O}}}_{2}]}_{{mathrm{bulk}}}left(1-frac{1}{1+{e}^{-gamma x}}right)right]}times {[{mathrm{N}}{{mathrm{O}}}_{3}^{-}]}_{{mathrm{bulk}}}left(1-frac{1}{1+{e}^{-delta (x-varepsilon )}}right)$$whereby α, β, γ, δ, and ε are fitting parameters and x is the distance to the particle edge. (Supplementary Note). The shape of this fit prediction matches the observed empirical data remarkably well (Fig. 3d, Supplementary Fig. 5), and the midpoints and inflection points from one timepoint to the next were significantly different (one-way ANOVA, F 1,8 = 27.9, p = 4.0 (times) 10−11 for Nar midpoints, F 1,8 = 14.9, p = 4.5 (times) 10−8 for Nir midpoints, F 1,8 = 17.2, p = 9.5 (times) 10−9 for Nar inflection points, and F 1,8 = 21.0, p = 1.0 (times) 10−9 for Nir inflection points). These fits illustrate how the response of PAO1 nar and nir gene expression reflects the balance between bacterial consumption and diffusion of O2 and NO3– from bulk surrounding fluid. In this manner, the fluorescence signal diminishes after achieving its peak and microcolonies remain small behind the advancing fluorescence wave while colonies continue to expand ahead of it (Supplementary Fig. 5, Supplementary Fig. 8). Nar was downregulated in the wake of the wave, causing microcolony expansion to slow or cease in the absence of respiration. Putatively, continued O2 and then NO3– uptake by large microcolonies at the periphery created growth limitation for microcolonies in their shadows farther from the bulk fluid source. Nir expression also advanced as a wave but interestingly created an annulus of maximal expression bounded by lower expression toward both the periphery and center of the particle. This expression pattern likely reflects localized production and utilization of NO2– within the particle interior. Exterior to the ring, ample NO3– from the bulk favored nitrate reductase, but interior to the ring, NO3– and NO2– were diffusion-limited.Importantly, the heterogeneous distribution of Nar and Nir expression across the particle also manifested among PAO1 cells at the scale of individual microcolonies. High magnification colony-scale images near the particle periphery and in the transition zone revealed a common phenotype reflecting the overall expression across the particle whereby the core of a single colony is expressive but the outer margin is not. As quantified for >103 microcolonies per particle, the subregion expressing Nar or Nir (i.e., “on”) varied with distance from the particle edge (Fig. 5). A thin radial zone within the particle (distance from the particle edge, x ~90–240 µm) harbored high heterogeneity with colonies ranging from 0–100% as shown (Fig. 5b, c). This narrow transition zone aligned with that quantified at lower magnification (Figs. 3, 4), indicative of a sharp transition from O2 to nitrate- and nitrite-driven respiration. In the flanking region exterior to this zone (x  240 µm), Nar was predominately on (median colony fraction expression = 0.78 ± 0.40 interquartile range; Fig. 5b) and Nir was almost completely on (median colony fraction expression = 0.95 ± 0.25 interquartile range; Fig. 5c). These expression characteristics resulted in a significantly stronger population bimodality for Nir than for Nar (Fig. 5d); Kolmogorov–Smirnov nonparametric test for probability distribution similarity, n1 = 1554, n2 = 1354, p = 1.2 (times) 10−40, Dn = 0.25. Akin to the annular feature observed at lower magnification (Fig. 3), this binary Nir expression likely reflects localized endogenous production and utilization of NO2–. Since NO2– is not continuously supplied from bulk media via lateral external diffusion like NO3–, microcolonies in the interior use Nar to produce NO2–, which is then preferentially consumed via Nir within each microcolony as the next most available oxidant for generating energy.Fig. 5: Heterogenous denitrification gene expression within individual microcolonies.a PAO1 NarK-GFP (top) or NirS-dsRed (bottom) were grown in separate particles in LB media saturated with 50% air and supplemented with 40 μM NO3–. Arrows indicate the particle edge; scale bars = 100 μm. For hundreds of microcolonies, the fraction expressing either NarK or NirS was quantified, and example microcolonies (right) illustrate ‘on’ fractions ranging from 0 to 0.90, with ‘on’ subregions noted by blue dotted lines. b Microcolony fraction expression for NarK-GFP and c NirS-dsRed, as a function of distance to the nearest particle edge. Shown are the median (blue crosses) and quartiles binned over 25 μm of radial particle space. Colonies were primarily ‘off’ in the aerated zone nearest bulk fluid (x  250 μm). In the aerobic zone, the median expression fraction of NarK-GFP is 3.1 × 10−5 (interquartile range 5.5 × 10−5) whereas the NirS-dsRed expression fraction is 2.7 × 10−5 (interquartile range 1.5 × 10−5). Expression of both genes are not significantly different from each other (Wilcoxon rank sum; p = 0.1). In the anaerobic zone, the median expression fraction of NarK-GFP and NirS-dsRed are 0.70 (interquartile 0.39) and 0.94, (interquartile 0.24), respectively. These anoxic fractional expressions are significantly different from each other (n1 = 1160, n2 = 1078, Wilcoxon rank sum; p = 2.2 × 10−32, w = 2724). The occurrence of heterogenous partially-on microcolonies reflects a sharp transition zone between presumptive aerobic and anoxic conditions (x ~ 100–250 μm). d Probability density functions for each reporter strain indicate stronger bimodality and higher binary expression for NirS-dsRed than for NarK-GFP. The distribution of NarK v. NirS expression are significantly different from each other (two sample Kolmogorov–Smirnov test; n1 = 1554, n2 = 1354 p = 1.2 × 10−40, Dn = 0.25) with significantly different medians (n1 = 1554, n2 = 1354, Wilcoxon rank sum; p = 1.9 × 10−33, w = 1.9 × 106).Full size imageRespiratory shading by exterior colonies and cells was a key emergent feature among microcolonies within the agarose particles, occurring in a fractal-like geometry. At the particle-scale ((R) ~1500 µm), nutrient consumption by microcolonies at the periphery prevented uptake by those at the interior; similarly, at the microcolony-scale (r ~25 µm), cells at the margin prevented uptake by those at the center, with consumption generating gradients of uptake flux at both scales. Shading did not occur for microcolonies in the particle core through the early stages of suboxia, as Nar and Nir expression were absent and microcolony size distribution was uniform over the first 14 h (Fig. 3, Supplementary Fig. 8). Since microcolonies were small over this stage, the O2 flux to the center outpaced aerobic respiration, and cell biomass at the particle-scale had not yet substantially diminished diffusive O2 availability. Then, owing to cell growth and the onset of shading, the O2 concentrations decreased rapidly over ~2 h (Fig. 4). As such, respiratory shading should be diminished when the density of microcolonies is lower. We tested this hypothesis with particles seeded at very low density (~102 cells mL−1) resulting in 1–6 microcolonies particle−1. Indeed, after 40 h of growth, the resultant microcolonies were quite large (r = 62 ± 11 µm (sd)); Supplementary Fig. 9) regardless of radial location within the particle, reflecting low intercolony competition for oxidants that readily diffused throughout the particle. While here respiratory shading across scales occurred for a clonal population, natural multispecies communities may additionally distribute functional roles among diverse taxa, e.g., anammox aggregates spatially differentiate such that aerotolerant species encase the outer perimeter to respire O2, shielding Planctomycetes to perform oxygen-inhibited anammox in the interior10. More

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    Crop origins explain variation in global agricultural relevance

    1.FAOSTAT: Crops (FAO, 2109); http://www.fao.org/faostat/en/#data/QC2.Mottet, A. et al. Livestock: on our plates or eating at our table? A new analysis of the feed/food debate. Glob. Food Sec. 14, 1–8 (2017).Article 

    Google Scholar 
    3.Prescott-Allen, R. & Prescott-Allen, C. How many plants feed the world? Conserv. Biol. 4, 365–374 (1990).Article 

    Google Scholar 
    4.Crittenden, A. N. & Schnorr, S. L. Current views on hunter-gatherer nutrition and the evolution of the human diet. Am. J. Phys. Anthropol. 162, 84–109 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Khoury, C. K. et al. Origins of food crops connect countries worldwide. Proc. R. Soc. B 283, 20160792 (2016).Article 

    Google Scholar 
    6.Poisot, T., Canard, E., Mouquet, N. & Hochberg, M. E. A comparative study of ecological specialization estimators. Methods Ecol. Evol. 3, 537–544 (2012).Article 

    Google Scholar 
    7.Ray, D. K., Mueller, N. D., West, P. C. & Foley, J. A. Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8, e66428 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Khoury, C. K. et al. Increasing homogeneity in global food supplies and the implications for food security. Proc. Natl Acad. Sci. USA 111, 4001–4006 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Newton, A. C., Johnson, S. N. & Gregory, P. J. Implications of climate change for diseases, crop yields and food security. Euphytica 179, 3–18 (2011).Article 

    Google Scholar 
    11.Hawkesworth, S. et al. Feeding the world healthily: the challenge of measuring the effects of agriculture on health. Philos. Trans. R. Soc. B 365, 3083–3097 (2010).Article 

    Google Scholar 
    12.Popkin, B. M. Technology, transport, globalization and the nutrition transition food policy. Food Policy 31, 554–569 (2006).Article 

    Google Scholar 
    13.Spengler III, R. N. Fruit from the Sands: The Silk Road Origins of the Foods We Eat (Univ. of California Press, 2019).14.Vaughan, J. & Geissler, C. The New Oxford Book of Food Plants (Oxford Univ. Press, 2009).15.Purugganan, M. D. & Fuller, D. Q. The nature of selection during plant domestication. Nature 457, 843–848 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Wang, L. et al. The interplay of demography and selection during maize domestication and expansion. Genome Biol. 18, 215 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    17.Milla, R., Bastida, J. M., Turcotte, M. M. & Al, E. Phylogenetic patterns and phenotypic profiles of the species of plants and mammals farmed for food. Nat. Ecol. Evol. 2, 1808–1817 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Ellis, E. C., Klein Goldewijk, K., Siebert, S., Lightman, D. & Ramankutty, N. Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr. 19, 589–606 (2010).
    Google Scholar 
    19.Xu, C., Kohler, T. A., Lenton, T. M., Svenning, J.-C. & Scheffer, M. Future of the human climate niche. Proc. Natl Acad. Sci. USA 117, 11350–11355 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Harlan, J. R. Crops and Man (ASA, 1992).21.Blumler, M. A. et al. in The Origins and Spread of Agriculture and Pastoralism in Eurasia (ed. Harris, D. R.) 25–50 (Smithsonian Institution Press, 1996).22.Hancock, J. F. Plant Evolution and the Origin of Crop Species (CABI, 2012).23.Harlan, J. R. The Living Fields: Our Agricultural Heritage (Cambridge Univ. Press, 1998).24.Lombardo, U. et al. Early Holocene crop cultivation and landscape modification in Amazonia. Nature 581, 190–193 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Denham, T. et al. The domestication syndrome in vegetatively propagated field crops. Ann. Bot. 125, 581–597 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Meyer, R. S., DuVal, A. E. & Jensen, H. R. Patterns and processes in crop domestication: an historical review and quantitative analysis of 203 global food crops. New Phytol. 196, 29–48 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Milla, R. Crop origins and phylo food: a database and a phylogenetic tree to stimulate comparative analyses on the origins of food crops. Glob. Ecol. Biogeogr. 29, 606–614 (2020).Article 

    Google Scholar 
    28.Larson, G. et al. Current perspectives and the future of domestication studies. Proc. Natl Acad. Sci. USA 111, 6139–6146 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Esquinas-Alcázar, J. Protecting crop genetic diversity for food security: political, ethical and technical challenges. Nat. Rev. Genet. 6, 946–953 (2005).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Clement, C. R. 1492 and the loss of Amazonian crop genetic resources. I. The relation between domestication and human population decline. Econ. Bot. 53, 188–202 (1999).Article 

    Google Scholar 
    31.Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).Article 

    Google Scholar 
    32.Tauger, M. B. Agriculture in World History (Routledge, 2013).33.Futuyma, D. J. & Moreno, G. The evolution of ecological specialization. Annu. Rev. Ecol. Syst. 19, 207–233 (1988).Article 

    Google Scholar 
    34.Forister, M. L., Dyer, L. A., Singer, M. S., Stireman, J. O. III & Lill, J. T. Revisiting the evolution of ecological specialization, with emphasis on insect–plant interactions. Ecology 93, 981–991 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Colles, A., Liow, L. H. & Prinzing, A. Are specialists at risk under environmental change? Neoecological, paleoecological and phylogenetic approaches. Ecol. Lett. 12, 849–863 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Richerson, P. J., Boyd, R. & Bettinger, R. L. Was agriculture impossible during the Pleistocene but mandatory during the Holocene? A climate change hypothesis. Am. Antiq. 66, 387–411 (2001).Article 

    Google Scholar 
    38.Mueller, U. G. & Rabeling, C. A breakthrough innovation in animal evolution. Proc. Natl Acad. Sci. USA 105, 5287–5288 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Schultz, T. R. & Brady, S. G. Major evolutionary transitions in ant agriculture. Proc. Natl Acad. Sci. USA 105, 5435–5440 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Mueller, U. G., Scott, J. J., Ishak, H. D., Cooper, M. & Rodrigues, A. Monoculture of leafcutter ant gardens. PLoS ONE 5, e12668 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    41.Kingsbury, N. Hybrid, the History and Science of Plant Breeding (Univ. of Chicago Press, 2009).42.Food Outlook—Biannual Report on Global Food Markets: June 2020 (FAO, 2020).43.van Kleunen, M. et al. Economic use of plants is key to their naturalization success. Nat. Commun. 11, 3201 (2020).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    44.Li, T. et al. Domestication of wild tomato is accelerated by genome editing. Nat. Biotechnol. 36, 1160–1163 (2018).CAS 
    Article 

    Google Scholar 
    45.Siddique, K. H. M., Li, X. & Gruber, K. Rediscovering Asia’s forgotten crops to fight chronic and hidden hunger. Nat. Plants 7, 116–122 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Lancaster, L. T. Host use diversification during range shifts shapes global variation in Lepidopteran dietary breadth. Nat. Ecol. Evol. 4, 963–969 (2020).47.Milla, R. Crop Origins and Phylo Food (GitHub, accessed 1 December 2020); https://github.com/rubenmilla/Crop_Origins_Phylo48.Global Biodiversity Information Facility (GBIF, 2018); https://www.gbif.org49.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    50.Paradis, E., Claude, J. & Strimmer, K. {APE}: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
    Article 

    Google Scholar 
    51.Martin, A. R. et al. Regional and global shifts in crop diversity through the Anthropocene. PLoS ONE 14, e0209788 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.The Plant List Version 2 (2013); http://www.theplantlist.org/53.Cayuela, L., la Cerda, Í. G., Albuquerque, F. S. & Golicher, D. J. taxonstand: an R package for species names standardisation in vegetation databases. Methods Ecol. Evol. 3, 1078–1083 (2012).Article 

    Google Scholar 
    54.Beres, B. L. et al. A systematic review of durum wheat: enhancing production systems by exploring genotype, environment, and management (Gx Ex M) synergies. Front. Plant. Sci. 11, 568657 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Paradis, E. in Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology (ed. Garamszegi, L. Z.) 3–18 (Springer, 2014).56.Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    57.Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2011).Article 

    Google Scholar 
    58.de Villemereuil, P. & Nakagawa, S. in Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology (ed. Garamszegi, L. Z.) 287–304 (Springer, 2014).59.Keck, F., Rimet, F., Bouchez, A. & Franc, A. phylosignal: an R package to measure, test, and explore the phylogenetic signal. Ecol. Evol. 6, 2774–2780 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Bush, S. E. et al. Unlocking the black box of feather louse diversity: a molecular phylogeny of the hyper-diverse genus Brueelia. Mol. Phylogenet. Evol. 94, 737–751 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).62.Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2019).63.Grafen, A. & Hamilton, W. D. The phylogenetic regression. Philos. Trans. R. Soc. Lond. B 326, 119–157 (1989).CAS 
    Article 

    Google Scholar 
    64.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Development Core Team. nlme: Linear and nonlinear mixed effects models. R package version 3.1-142 (2020).65.Ives, A. R. & Garland, T. Jr. Phylogenetic logistic regression for binary dependent variables. Syst. Biol. 59, 9–26 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Earliest evidence of marine habitat use by mammals

    1.McCrea, R. T., Pemberton, S. G. & Currie, P. J. New ichnotaxa of mammal and reptile tracks from the Upper Paleocene of Alberta. Ichnos 11, 323–339 (2004).Article 

    Google Scholar 
    2.Henderson, D. M. A wide-gauge, large-mammal trackway from the upper Paleocene of Alberta Canada. Can. J. Earth Sci. 52, 696–700 (2015).ADS 
    Article 

    Google Scholar 
    3.Lüthje, C. J., Milàn, J. & Hurum, J. H. Paleocene tracks of the mammal pantodont genus Titanoides in coal-bearing strata, Svalbard, Arctic Norway. J. Vertebr. Paleontol. 30, 521–527 (2010).Article 

    Google Scholar 
    4.Davydenko, S., Laime, M. J. & Gol’din, P. The earliest record of a marine mammal (Cetacea: Basilosauridae) from the Eocene of Amazonia. J. Vertebr. Paleontol. 38, e1549060 (2019).Article 

    Google Scholar 
    5.Hansen, D.E. Laramide tectonics and deposition of the Ferris and Hanna Formations, south-central Wyoming in Paleotectonics and sedimentation in the Rocky Mountain Region, United States: American Association of Petroleum Geologists Memoir 41 (ed. Peterson, J.A.) 481–495 (AAPG, 1986).6.Dechesne, M. et al. A new stratigraphic framework and constraints for the position of the Paleocene-Eocene boundary in the rapidly subsiding Hanna Basin, Wyoming. Geosphere 16, 594–618 (2020).ADS 
    Article 

    Google Scholar 
    7.Hasiotis, S. T. & Honey, J. G. Paleohydrologic and stratigraphic significance of crayfish burrows in continental deposits: examples from several Paleocene Laramide basins in the Rocky Mountains. J. Sediment. Res. 70, 127–139 (2000).ADS 
    CAS 
    Article 

    Google Scholar 
    8.Gingras, M. K., Pemberton, S. G., Saunders, T. D. A. & Clifton, H. E. The ichnology of modern and Pleistocene brackish-water deposits at Willapa Bay, Washington: variability in estuarine settings. Palaios 14, 352–374 (1999).ADS 
    Article 

    Google Scholar 
    9.Gingras, M. K., Hubbard, S. M., Pemberton, S. G. & Saunders, T. The significance of Pleistocene Psilonichnus at Willapa Bay, Washington. Palaios 15, 142–151 (2000).ADS 
    Article 

    Google Scholar 
    10.Gingras, M.K., MacEachern, J.A., Dashtgard, S.E., Zonneveld, J.-P., Schoengut, J., Ranger, M.J., & Pemberton, G. Estuaries. in Trace fossils as indicators of sedimentary environments. Developments in sedimentology, Volume 64 (eds. Knaust, D. &Bromley, R.G.) 463–507 (Elsevier, 2012).11.Gingras, M. K., MacEachern, J. A., Dashtgard, S. E., Ranger, M. J. & Pemberton, S. G. The significance of trace fossils in the McMurray Formation, Alberta, Canada. Bull. Can. Pet. Geol. 64, 233–250 (2016).Article 

    Google Scholar 
    12.MacEachern, J.A., Bann, K.L., Bhattacharya, J., & Howell, C.D. Ichnology of deltas: organism responses to the dynamic interplay of rivers, waves, storms, and tides. in River deltas-concepts, models, and examples, Volume 51, SEPM Special Publication (eds. Giosan, L., & Bhattacharya, J.P.) 49–85 (SEPM, 2005).13.Hauk, T. E., Dashtgard, S. E. & Pemberton, S. G. Brackish-water ichnological trends in a microtidal barrier island–embayment system, Kouchibouguac National Park, New Brunswick, Canada. Palaios 24, 478–496 (2011).ADS 
    Article 

    Google Scholar 
    14.Pemberton, S.G., & Wightman, D.M. Ichnological characteristics of brackish water deposits. in Applications of ichnology to petroleum exploration. Volume 17, SEPM Core Workshops, (ed. Pemberton, S.G.) 141–167 (SEPM, 1992).15.Hubbard, S. M., Gingras, M. K. & Pemberton, S. G. Palaeoenvironmental implications of trace fossils in estuary deposits of the Cretaceous Bluesky Formation, Cadotte region, Alberta, Canada. Fossils Strata 51, 68–87 (2004).
    Google Scholar 
    16.Xing, L. et al. Dinosaur natural track casts from the Lower Cretaceous Hekou Group in the Lanzhou-Minhe Basin, Gansu, Northwest China: Ichnology, track formation, and distribution. Cretac. Res. 52, 194–205 (2015).Article 

    Google Scholar 
    17.Elbroch, M. Mammal tracks and sign (Stackpole Books, 2003).
    Google Scholar 
    18.Osborn, H. F. Evolution of the Amblypoda. Part I. Taligrada and Pantodonta. Bull. Am. Mus. Nat. Hist. Bull. 10, 1–50 (1898).
    Google Scholar 
    19.Simons, E. L. The Paleocene Pantodonta. Trans. Am. Philos. Soc. 50, 3–99 (1960).Article 

    Google Scholar 
    20.Bennett, M. R., Morse, S. A. & Falkingham, P. L. Tracks made by swimming Hippopotami: an example from Koobi Fora (Turkana Basin, Kenya). Palaeogeogr. Palaeoclimatol. Palaeoecol. 409, 9–23 (2014).Article 

    Google Scholar 
    21.Clementz, M. T., Holroyd, P. A. & Koch, P. L. Identifying aquatic habits of herbivorous mammals through stable isotope analysis. Palaios 23, 574–585 (2008).ADS 
    Article 

    Google Scholar 
    22.Uhen, M. D. & Gingerich, P. D. Evolution of Coryphodon (Mammalia, Pantodonta) in the late Paleocene and early Eocene of northwestern Wyoming. Contrib. Mus. Paleontol. Univ. Michigan 29, 259–289 (1995).
    Google Scholar 
    23.Hasiotis, S. T. Reconnaissance of Upper Jurassic Morrison Formation ichnofossils, Rocky Mountain Region, USA: paleoenvironmental, stratigraphic, and paleoclimatic significance of terrestrial and freshwater ichnocoenoses. Sed. Geol. 167, 177–268 (2004).Article 

    Google Scholar 
    24.Bordy, E. M., Bumby, A. J., Catuneanu, O. & Eriksson, P. G. Possible trace fossils of putative termite origin in the Lower Jurassic (Karoo Supergroup) of South Africa and Lesotho. S. Afr. J. Sci. 105, 356–362 (2009).
    Google Scholar 
    25.Bromley, R. G. et al. Comments on the paper “Reconnaissance of Upper Jurassic Morrison Formation ichnofossils, Rocky Mountain Region, USA: Paleoenvironmental, stratigraphic, and paleoclimatic significance of terrestrial and freshwater ichnocoenoses” by Stephen T. Hasiotis. Sed. Geol. 200, 141–150 (2007).Article 

    Google Scholar 
    26.Eberle, J. J. A new ‘tapir’ from Ellesmere Island, Arctic Canada-implications for northern high latitude palaeobiogeography and tapir palaeobiology. Palaeogeogr. Palaeoclimatol. Palaeoecol. 227, 311–322 (2004).Article 

    Google Scholar 
    27.Halliday, T. J. D., Upchurch, P. & Goswami, A. Resolving the relationships of Paleocene placental mammals. Biol. Rev. 92, 521–550 (2017).Article 

    Google Scholar 
    28.Zurano, J. P. et al. Cetrtiodactyla: updating a time-calibrated molecular phylogeny. Mol. Phylogenet. Evol. 133, 256–262 (2019).Article 

    Google Scholar 
    29.Knaust, D. Atlas of trace fossils in well core: appearance, taxonomy and interpretation (Springer, 2017).Book 

    Google Scholar 
    30.Bingham, B. L., Freytes, I., Emery, M., Dimond, J. & Muller-Parker, G. Aerial exposure and body temperature of the intertidal sea anemone Anthopleura elegantissima. Invertebr. Biol. 130, 291–301 (2011).Article 

    Google Scholar 
    31.Jayewardene, J. The elephant in Sri Lanka. Wildlife Heritage Trust of Sri Lanka, Sri Lanka (1994).32.Miller, F.L. Inter-island water crossings by Peary caribou, south-central Queen Elizabeth Islands. Arctic, 8–12 (1995).33.Harveson, P. M., Grant, W. E., Lopez, R. R., Silvy, N. J. & Frank, P. A. The role of dispersal in Florida Key deer metapopulation dynamics. Ecol. Model. 195, 393–401 (2006).Article 

    Google Scholar 
    34.Quigley, D. T. G. & Moffatt, S. Sika-like deer Cervus nippon Temminck, 1838 observed swimming out to sea at Greystones, Co., Wicklow: increasing deer population pressure?. Bull. Ir. Biogeogr. Soc. 38, 251–261 (2014).
    Google Scholar 
    35.Castelló, J. R. Bovids of the world: Antelopes, gazelles, cattle, goas, sheep, and relatives (Princeton University Press, 2016).Book 

    Google Scholar 
    36.Naranjo, E.J. Tapirs of the Neotropics. in Ecology and conservation of tropical ungulates in Latin America (ed. Gallina-Tessaro, S.) 439–451(Springer, 2019).37.Kavčić, K., Corlatti, L., Rodriguez, O., Kavčić, B. & Šprem, N. From the mountains to the sea! Unusual swimming behavior in chamois Rupicapra spp. Ethol. Ecol. Evol. 32, 402–408 (2020).Article 

    Google Scholar 
    38.Roth, H. H., Hoppe-Dominik, B., Mühlenberg, M., Steinhauer-Burkart, B. & Fischer, F. Distribution and status of the hippopotamids in the Ivory Coast. Afr. Zool. 39, 211–224 (2004).Article 

    Google Scholar 
    39.Pilfold, N. W., McCall, A., Derocher, A. E., Lunn, N. J. & Richardson, E. Migratory response of polar bears to sea ice loss: to swim or not to swim. Ecography 40, 189–199 (2017).Article 

    Google Scholar 
    40.Smith, T. S. & Partridge, S. T. Dynamics of intertidal foraging by coastal brown bears in southwestern Alaska. J. Wildl. Manag. 68, 233–240 (2004).Article 

    Google Scholar 
    41.Lewis, T. M. & Lafferty, D. J. Brown bears and wolves scavenge humpback whale carcass in Alaska. Ursus 25, 8–13 (2014).Article 

    Google Scholar 
    42.Morgan, B. J. & Lee, P. C. Forest elephant group composition, frugivory and coastal use in the Réserve de Faune du Petit Loango, Gabon. Afr. J. Ecol. 45, 519–526 (2007).Article 

    Google Scholar 
    43.Prinsloo, A. S., Pillay, D. & O’Riain, M. J. Multiscale drivers of hippopotamus distribution in the St Lucia Estuary, South Africa. Afr. Zool. 55, 127–140 (2020).Article 

    Google Scholar 
    44.Boonratana, R. A statewide survey to estimate the distribution and density of the Sumatran rhinoceros, Asian elephant and banteng in Sabah, Malaysia. Wildlife Conservation Society, New York (1997). More